Zonas Metropolitanas 2020
Migración reciente 2015 - 2020
Diana Villasana Ocampo
Cuestionario Ampliado del Censo de Población y Vivienda 2020
El cuestionario ampliado se guarda en un un archivo
.RData.
data <- read_sav("~/Personas_Censo 2020.SAV")
save(data,
file = paste0(here::here(), "/Bases/Censo_Personas_2020.RData"))Se seleccionan las variables que se desean conservar para la
realización de este documento y se guarda en un archivo
.RData para practicidad del manejo de datos.
load(paste0(here::here(), "/Bases/Censo_Personas_2020.RData"))
mydata <- data %>%
select(CVE_ENT, ENT, MUN, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES15,
ENT_PAIS_NAC, ENT_PAIS_TRAB, MUN_TRAB, CVE_MUN_TRABAJO,
ENT_PAIS_ASI, MUN_ASI, CVE_MUN_ASI, EDAD, SEXO, AFRODES, HLENGUA, QDIALECT_INALI,
PERTE_INDIGENA, ALFABET, CAUSA_MIG, SITUA_CONYUGAL, HIJOS_NAC_VIVOS,
CONACT, OCUPACION_C, SITTRA, VACACIONES, SERVICIO_MEDICO, INCAP_SUELDO, INGTRMEN,
ACTIVIDADES_C, TIE_TRASLADO_TRAB, MED_TRASLADO_TRAB1, MED_TRASLADO_TRAB2, MED_TRASLADO_TRAB3,
ASISTEN, NIVACAD, ESCOLARI, ESCOACUM, NOMCAR_C, TIE_TRASLADO_ESCU,
MED_TRASLADO_ESC1, MED_TRASLADO_ESC2, MED_TRASLADO_ESC3,
FACTOR, ESTRATO, UPM)Zonas Metropolitanas 2020
Se anexa la base de datos de las Zonas Metropolitanas 2020 a la base orginal
ZM_2020 <- read.xlsx(paste0(here::here(), "/Bases/Municipio/ZM_2020.xlsx"),
startRow = 7,
skipEmptyRows = TRUE) %>%
select(CVE_ZM, NOM_ZM, CVE_ENT, NOM_ENT, CVE_MUN, NOM_MUN, MC, CF) %>%
mutate(CVE_ENT = stringr::str_pad(.$CVE_ENT, width = 3, side = c("left"), pad = "0"),
CVE_MUN = stringr::str_pad(.$CVE_MUN, width = 6, side = c("left"), pad = "0"))Se asignan las claves de las zonas metropolitanas de acuerdo a las diferentes variables de interes: - Residencia hace 5 años - Laboral - Estudiantil - Nacimiento
mydata <- mydata %>%
rename("CVE_MUN_RES" = "CVE_MUN_RES15") %>%
# Zonas Metropolitanas por residenicia
left_join(., ZM_2020 %>% select(-CVE_ENT), by = c("CVE_MUN")) %>%
# Zonas Metropolitanas en el lugar de residencia hace 5 años
left_join(., ZM_2020 %>% select(-CVE_ENT, -NOM_ENT, -NOM_MUN) %>%
rename("CVE_ZM_RES" = "CVE_ZM",
"ZM_RES" = "NOM_ZM",
"MC_RES" = "MC",
"CF_RES" = "CF"), by = c("CVE_MUN_RES" = "CVE_MUN")) %>%
# Zonas Metropolitanas en el lugar de trabajo
left_join(., ZM_2020 %>% select(-CVE_ENT, -NOM_ENT, -NOM_MUN) %>%
rename("CVE_ZM_TRABAJO" = "CVE_ZM",
"ZM_TRABAJO" = "NOM_ZM",
"MC_TRABAJO" = "MC",
"CF_TRABAJO" = "CF"), by = c("CVE_MUN_TRABAJO" = "CVE_MUN")) %>%
# Zonas Metropolitanas en el lugar de estudio
left_join(., ZM_2020 %>% select(-CVE_ENT, -NOM_ENT, -NOM_MUN) %>%
rename("CVE_ZM_ASI" = "CVE_ZM",
"ZM_ASI" = "NOM_ZM",
"MC_ASI" = "MC",
"CF_ASI" = "CF"), by = c("CVE_MUN_ASI" = "CVE_MUN"))
save(mydata, file = paste0(here::here(), "/Bases/06_Migracion por Zonas Metropolitanas_2020.RData")) ✔️A partir de aquí se pueden correr los códidos 👇. Se carga el
archivo Migracion por Zonas Metropolitanas_2020.RData.
load(file = paste0(here::here(), "/Bases/06_Migracion por Zonas Metropolitanas_2020.RData"))
# Para fines prácticos se genera un ponderador de uno
mydata <- mydata %>%
select(CVE_ENT, ENT, MUN, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES,
EDAD, CVE_ZM, NOM_ZM, NOM_ENT, NOM_MUN, MC, CF, CVE_ZM_RES, ZM_RES,
MC_RES, CF_RES, FACTOR, ESTRATO, UPM) %>%
mutate(M = 1) %>%
mutate(NOM_ENT = as.factor(.$CVE_ENT)) %>%
ungroup()
# Se vuelve a cargar la base de datos para fines practicos
ZM_2020 <- read.xlsx(paste0(here::here(), "/Bases/Municipio/ZM_2020.xlsx"),
startRow = 7,
skipEmptyRows = TRUE) %>%
mutate(CVE_ENT = stringr::str_pad(.$CVE_ENT, width = 3, side = c("left"), pad = "0"),
CVE_MUN = stringr::str_pad(.$CVE_MUN, width = 6, side = c("left"), pad = "0"))Entidades y Municipios
Se genera un vector con el nombre de las entidades llamado
estados para facilitar los filtros en el documento.
Se genera un vector con las abreviaturas de las entidades llamado
ent para fines prácticos.
Se genera un vector con las claves de los municipios, pero es importante
hacer notar que tres municipios no entraron el muestreo del Cuestionario
Ampliado.
# Claves de los estados
estados <- sjlabelled::get_labels(mydata$CVE_ENT)
nom_estados <- c( "Aguascalientes", "Baja California" ,"Baja California Sur", "Campeche", "Coahuila de Zaragoza", "Colima",
"Chiapas", "Chihuahua", "Ciudad de México", "Durango", "Guanajuato", "Guerrero", "Hidalgo", "Jalisco",
"México", "Michoacán de Ocampo", "Morelos", "Nayarit", "Nuevo León", "Oaxaca", "Puebla", "Querétaro",
"Quintana Roo", "San Luis Potosí", "Sinaloa", "Sonora", "Tabasco", "Tamaulipas", "Tlaxcala",
"Veracruz de Ignacio de la Llave", "Yucatán", "Zacatecas")
est <- c("AGS", "BC", "BCS", "CAMP", "COAH", "COL", "CHIS", "CHIH", "CDMX", "DGO", "GTO", "GRO", "HGO",
"JAL", "MEX", "MICH", "MOR", "NAY", "NL", "OAX", "PUE", "QRO", "QROO", "SLP","SIN","SON", "TAB",
"TAMS", "TLX", "VER", "YUC", "ZAC")
# Claves de los municipios
MUN <- readRDS(paste0(here::here(), "/Bases/municipios_2020.RDS"))
nom_municipios <- sjlabelled::get_labels(MUN$NOM_MUN) %>% as.factor()
municipios <- sjlabelled::get_labels(MUN$CVE_MUN) %>% as.factor()
#saveRDS(MUN, file = paste0(here::here(), "/Bases/municipios_2020.RDS"))
# Claves de las zonas metropolitanas
zm <- sjlabelled::get_labels(mydata$CVE_ZM)[-2]
nom_zm <- sjlabelled::get_labels(mydata$NOM_ZM)[-2]Migración reciente
Migración interna
Matrices
Se utiliza la paquetería survey para poder trabajar con
la muestra del cuestionario ampliado, en la cual se selecciona a la
población de 5 años y más.
options(survey.lonely.psu = "adjust")
MC <- mydata %>%
select(CVE_ENT, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES, CVE_ZM, MC, CF, CVE_ZM_RES, M, EDAD, FACTOR, ESTRATO, UPM) %>%
# Se genera una indicadora de zm
mutate(I_ZM_2020 = ifelse(is.na(.$CVE_ZM), '0', '1'),
I_RES5A_ZM_2020 = ifelse(is.na(.$CVE_ZM_RES), '0', '1')) %>%
# Se clasifican a los migrantes internos
mutate(I_ZM = case_when(.$CVE_MUN == .$CVE_MUN_RES ~ 'Pertenecen a la Zona Metropolitana', #Residentes
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM == .$CVE_ZM_RES ~ "Pertenecen a la Zona Metropolitana", #Migrantes que residen en la misma zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM != .$CVE_ZM_RES ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residen en otra zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian fuera de la zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '1' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian dentro de una zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana' #Migrantes que no residen en la zona metropolitana
)) %>%
filter(EDAD >= 5 & EDAD <= 130) %>%
filter(CVE_MUN_RES %in% municipios) %>%
svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)
saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_municipal.RDS"))MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_municipal.RDS"))
Migrantes <- svytable(~CVE_MUN_RES + CVE_MUN, design = MC) Se genera la matriz cuadrada y se le asignan los nombres de los estados.
Migrantes <- Migrantes %>%
as.data.frame() %>%
expss::cross_cases(CVE_MUN, CVE_MUN_RES, weight = Freq) %>%
as.data.frame() %>%
rename("CVE_MUN" = "row_labels") %>%
arrange(CVE_MUN) %>%
slice(-1)
rownames <- Migrantes %>%
mutate(CVE_MUN = substr(.$CVE_MUN, 9, 16)) %>%
pull(CVE_MUN)
colnames <- names(Migrantes) %>%
as.data.frame() %>%
slice(-1) %>%
rename("CVE_MUN" = ".") %>%
mutate(`CVE_MUN` = substr(.$CVE_MUN, 13, 20)) %>%
pull(CVE_MUN)
# Se elimina la variable CVE_MUN
Migrantes <- Migrantes %>%
select(-CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
saveRDS(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RDS"))
save(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
require(openxlsx)
wb <- createWorkbook()
addWorksheet(wb, "M.Reciente")
writeData(wb, 1, Migrantes %>% as.data.frame() %>% tibble::rownames_to_column(var = "CVE_MUN"), colNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.xlsx"), overwrite = TRUE)Matriz de migración reciente hace 5 años a nivel municipal, 2015 - 2020
| Matriz de migración reciente 2015 - 2020 | |||||||||||||||||||||||||||||
| Nivel municipal | |||||||||||||||||||||||||||||
| CVE_MUN | 001001 | 001002 | 001003 | 001004 | 001005 | 001006 | 001007 | 001008 | 001009 | 001010 | 001011 | 002001 | 002002 | 002003 | 002004 | 002005 | 002006 | 003001 | 003002 | 003003 | 003008 | 003009 | 004001 | 004002 | 004003 | 004004 | 004005 | 004006 | 004007 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||||||||||||||||||||
Gráficos
ChordDiagram
Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
# Nombre de las Zonas Metropolitanas
NOM_ZM <- stringr::str_wrap(nom_zm, 100)
## Tomamos las Zonas Metropolitanas con más de 3 municipios que tienen flujos migratorios
#### Con filtro (CF)
ZM_CF <- ZM_2020 %>%
group_by(CVE_ZM) %>%
summarise(Count = n()) %>%
filter(Count >= 0) %>%
pull(CVE_ZM)
NOM_ZM_CF <- ZM_2020 %>%
filter(CVE_ZM %in% ZM_CF) %>%
distinct(CVE_ZM, NOM_ZM)
ZM <- lapply(1:length(ZM_CF), function(x){
ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% ZM_CF[x]) %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(NOM_MUN)
})
################################################################################
################################## Filtro ######################################
Inmigrantes_function <- function(ZM, Migrantes){
lapply(1:length(ZM), function(x){
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
})
}
Inmigrantes <- Inmigrantes_function(ZM, Migrantes)
Emigrantes_function <- function(ZM, Migrantes){
lapply(1:length(ZM), function(x){
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
})
}
Emigrantes <- Emigrantes_function(ZM, Migrantes)
################################## Filtro ######################################
#p <- data.frame(ZM = ZM_CF,
# filtro_municipio = tabla_municipios,
# filtro_estado = tabla_estados)
#write.xlsx(p, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel municipal.xlsx"), overwrite = TRUE)
#### Filtro de municipios
filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel municipal.xlsx"), colNames = TRUE) %>%
pull(filtro_municipio)
#### Filtro de estados
filtro_out <- read.xlsx(paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel municipal.xlsx"), colNames = TRUE) %>%
pull(filtro_estado)
################################################################################
tabla1 <- lapply(1:length(ZM_CF), function(x){
# filtro de municipios de la ZM
filtro <- Inmigrantes[[x]] %>%
full_join(., Emigrantes[[x]], by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > filtro_mig[x]) %>% ## Cambia el filtro de los municipios de la Zonas Metropolitanas
pull(rn)
# filtro de estados
filtro_est <- Inmigrantes[[x]] %>%
full_join(., Emigrantes[[x]], by = c("rn" = "cn")) %>%
filter(rn %nin% ZM[[x]]) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(value = sum(value)) %>%
filter(value >= filtro_out[x]) %>%
pull(rn)
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM[[x]] & .$rn %in% filtro ~ substr(.$rn, 2, nchar(.$rn)),
.$rn %in% ZM[[x]] & .$rn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$rn, 1, 3))], "ZM"), 100),
.$rn %nin% ZM[[x]] & substr(.$rn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]), 100),
.$rn %nin% ZM[[x]] & substr(.$rn, 1, 3) %nin% filtro_est ~ "Otros estados"),
cn = case_when(.$cn %in% ZM[[x]] & .$cn %in% filtro ~ substr(.$cn, 2, nchar(.$cn)),
.$cn %in% ZM[[x]] & .$cn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$cn, 1, 3))], "ZM"), 100),
.$cn %nin% ZM[[x]] & substr(.$cn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]), 100),
.$cn %nin% ZM[[x]] & substr(.$cn, 1, 3) %nin% filtro_est ~ "Otros estados")) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
}
)
## Se sacan los flujos migratorios que pertencen a otros estados
#tabla_estados <- sapply(1:length(ZM_CF), function(i){
# tabla1[[i]] %>%
# as.data.frame() %>%
# adorn_totals(c("row", "col"),
# fill = "-",
# na.rm = TRUE,
# ,,,,contains(colnames(tabla1[[i]]))) %>%
# select(`Otros estados`) %>%
# slice(nrow(.)) %>%
# mutate(`Otros estados` = .$`Otros estados`/4) %>%
# pull(`Otros estados`)
#})
## Se sacan los flujos migratorios que pertencen a otros municipios
#tabla_municipios <- sapply(1:length(ZM_CF), function(i){
# p <- tabla1[[i]] %>%
# as.data.frame() %>%
# select(-c(`Otros estados`)) %>%
# slice(-nrow(.))
# if(sum(p) == 0) {
# return(0)
# } else {
# p %>%
# adorn_totals(c("row", "col"),
# fill = "-",
# na.rm = TRUE,
# ,,,,contains(colnames(tabla1[[i]]))) %>%
# slice(nrow(.)) %>%
# mutate(Total = .$Total/50) %>%
# pull(Total)
# }
#})
## Se guardan las matrices de movilidad laboral para analizarlos después.
wb <- createWorkbook()
for(i in 1:length(ZM)){
tabla <- tabla1[[i]] %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(colnames(tabla1[[i]])))
addWorksheet(wb, paste(ZM_CF[i]))
writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel municipal_Reduccion.xlsx"),
overwrite = TRUE)
}
saveRDS(tabla1, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Tabla MR5a a nivel municipal.RDS"))tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Tabla MR5a a nivel municipal.RDS"))
total_tablas <- totales(tabla1, ZM)
porcentajes_tablas <- porcentajes(tabla1, ZM)
# Se guardan los totales de las matrices reducidas
wb <- createWorkbook()
for(i in 1:length(ZM)){
addWorksheet(wb, paste(ZM_CF[i]))
writeData(wb, i, total_tablas[[i]], colNames = TRUE, startCol = 1)
writeData(wb, i, porcentajes_tablas[[i]], colNames = TRUE, startCol = 5)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel municipal_Reduccion_Totales.xlsx"),
overwrite = TRUE)
}tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Tabla MR5a a nivel municipal.RDS"))
#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")
tabla2 <- color_chord_diagram(tabla1, ZM_CF = ZM_CF, paleta)file = "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a desagregado por ZM.pdf"
## Gráficos a nivel municipal
chord_diagram_graph(file = file,
width = 15,
height = 10,
family <- "Montserrat Medium",
paleta = paleta,
ZM_CF = ZM_CF,
tabla1,
tabla2,
color_labels = "#000C7D",
margin = c(0, 0, 0, 0))Etiquetas
Gráfico Sankey
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
# Nombre de las Zonas Metropolitanas
NOM_ZM <- stringr::str_wrap(nom_zm, 25)
## Tomamos las Zonas Metropolitanas con más de 3 municipios que tienen flujos migratorios
#### Con filtro (CF)
ZM_CF <- ZM_2020 %>%
group_by(CVE_ZM) %>%
summarise(Count = n()) %>%
filter(Count > 3) %>%
pull(CVE_ZM)
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(value > 0)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > 0)
filtro_est <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(value > 0)
################################################################################
tabla1 <- lapply(1:length(ZM_CF), function(x){
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% ZM_CF[x]) %>%
mutate(NOM_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(NOM_MUN)
# filtro de municipios metropolitanos
filtro <- filtro %>%
filter(rn %in% ZM) %>%
filter(value < 1000) %>%
pull(rn)
# filtro de estados
filtro_rn <- filtro_est %>%
mutate(value = ifelse(.$rn %nin% filtro |.$cn %nin% filtro, value, 0)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(Inm = sum(value, na.rm = TRUE))
filtro_cn <- filtro_est %>%
mutate(value = ifelse(.$rn %nin% filtro |.$cn %nin% filtro, value, 0)) %>%
mutate(cn = substr(.$cn, 1, 3)) %>%
group_by(cn) %>%
summarise(Emg = sum(value, na.rm = TRUE))
filtro_estados <- filtro_rn %>%
full_join(., filtro_cn, by = c("rn" = "cn")) %>%
mutate(value = sum_row(.$Inm, .$Emg, na.rm = TRUE)) %>%
filter(value > 5000) %>%
pull(rn)
tabla %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), .$value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %nin% filtro ~ .$rn,
.$rn %in% ZM & .$rn %in% filtro ~ paste(substr(.$rn, 1, 3), "Municipios ZM"),
.$rn %nin% ZM & substr(.$rn, 1, 3) %in% filtro_estados ~ paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]),
.$rn %nin% ZM & substr(.$rn, 1, 3) %nin% filtro_estados ~ "Otros estados"),
cn = case_when(.$cn %in% ZM & .$cn %nin% filtro ~ .$cn,
.$cn %in% ZM & .$cn %in% filtro ~ paste(substr(.$cn, 1, 3), "Municipios ZM"),
.$cn %nin% ZM & substr(.$cn, 1, 3) %in% filtro_estados ~ paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]),
.$cn %nin% ZM & substr(.$cn, 1, 3) %nin% filtro_estados ~ "Otros estados")) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE, fill = 0) %>%
column_to_rownames(., var = "rn")
}
)p <- lapply(1:length(ZM_CF), function(x){
tabla1[[x]] %>%
ggplot(aes(axis1 = rn,
axis2 = cn,
y = value), # c("value", "freq", "tasa")
reverse = FALSE,
na.rm = TRUE) +
geom_alluvium(aes(fill = rn),
curve_type = "quintic",
color = "transparent",
alpha = 0.85,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_stratum(aes(fill = cn),
color = "white",
alpha = 0.65,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 1, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = -.2,
min.segment.length = unit(1, "lines"),
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 2, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 2, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = .2,
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
theme_void() +
theme(plot.margin = margin(t = 1, r = 5, b = 1, l = 0, "cm"),
text = element_text(family = "montserrat"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 7, family = "montserrat"),
legend.position = c(1.02, .5)) +
scale_x_discrete(expand = c(-0.1, 0.5)) +
scale_fill_viridis_d(option = "A", end = 1, begin = 0.2) +
guides(fill = guide_legend(ncol = 1, na.translate = F)) +
labs(fill = "",
color = "")
}
)
path = paste0(here::here(),"/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/GSankey de MR5a desagregado por ZM_Absolutos.pdf")
ggexport(list = p, width = 18, height = 10, dpi = 400, filename = path)ZMVM
ChordDiagram
ChorDiagram sin grupos
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 30)) %>%
pull(NOM_MUN)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > 40000) %>%
pull(rn)
filtro_est <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
filter(rn %nin% ZM) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(value = sum(value)) %>%
filter(value >= 25000) %>%
pull(rn)
################################################################################
tabla1 <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM | .$cn %in% ZM) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM| .$cn %in% ZM), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %in% filtro ~ substr(.$rn, 2, nchar(.$rn)),
.$rn %in% ZM & .$rn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$rn, 1, 3))], "ZMVM"), 50),
.$rn %nin% ZM & substr(.$rn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]), 50),
.$rn %nin% ZM & substr(.$rn, 1, 3) %nin% filtro_est ~ "Otros estados"),
cn = case_when(.$cn %in% ZM & .$cn %in% filtro ~ substr(.$cn, 2, nchar(.$cn)),
.$cn %in% ZM & .$cn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$cn, 1, 3))], "ZMVM"), 50),
.$cn %nin% ZM & substr(.$cn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]), 50),
.$cn %nin% ZM & substr(.$cn, 1, 3) %nin% filtro_est ~ "Otros estados")) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn") cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a de ZMVM.pdf"),
width = 7,
height = 7,
family = "Montserrat Medium",
fallback_resolution = 400,
onefile = TRUE)
circos.clear()
circos.par(start.degree = 90,
clock.wise = TRUE,
gap.degree = 3,
track.margin = c(-0.2, 0.2),
points.overflow.warning = FALSE)
par(mar = rep(1.5, 4))
# Paleta de colores
groupColors <- setNames(rev(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1)))))),
nm = unique(c(colnames(tabla1), rownames(tabla1))))
chordDiagram(x = tabla1 %>% as.matrix(),
grid.col = groupColors,
order = union(rownames(tabla1), colnames(tabla1)),
keep.diagonal = FALSE,
symmetric = FALSE,
scale = FALSE,
transparency = 0.25,
directional = 1,
direction.type = c("arrows", "diffHeight"),
diffHeight = -0.04,
annotationTrack = "grid",
annotationTrackHeight = mm_h(c(3)), # ancho borde
preAllocateTracks = 1,
big.gap = 40,
link.arr.type = "big.arrow",
link.lwd = 0.5,
link.visible = TRUE,
link.largest.ontop = FALSE)
# Add text and axis
circos.trackPlotRegion(track.index = 1,
track.height = 0.05,
bg.border = NA,
panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
# Add names to the sector.
circos.text(x = mean(xlim),
y = ylim[1] + 0.2, #Ajusta a las etiquetas del origen
labels = sector.name,
facing = "clockwise",
niceFacing = TRUE,
adj = c(-0.01, 0.5), #Ajuste de las etiquetas (x, y)
cex = fontsize(7),
col = "#000C7D",
font = 1)
# Add graduation on axis
circos.axis(h = "top",
labels = c(0, 10, 20, 50, 100, 200, 300, 400, 500, 1000, seq(1000, 20000, by = 100000)),
major.tick.length = 0.2,
minor.ticks = 2,
labels.cex = fontsize(5),
sector.index = sector.name,
track.index = 2,
labels.niceFacing = TRUE,
labels.pos.adjust = c(0, 0.8))
}
)
dev.off()ChordDiagram con grupos
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(NOM_MUN)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > 30000) %>%
pull(rn)
filtro_est <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
filter(rn %nin% ZM) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(value = sum(value)) %>%
filter(value >= 25000) %>%
pull(rn)
################################################################################
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM | .$cn %in% ZM) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM| .$cn %in% ZM), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %in% filtro ~ substr(.$rn, 2, nchar(.$rn)),
.$rn %in% ZM & .$rn %nin% filtro ~ str_wrap(paste(substr(estados[as.numeric(substr(.$rn, 1, 3))], 2, 3), "ZMVM"), 100),
.$rn %nin% ZM & substr(.$rn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]), 100),
.$rn %nin% ZM & substr(.$rn, 1, 3) %nin% filtro_est ~ "Otros estados"),
cn = case_when(.$cn %in% ZM & .$cn %in% filtro ~ substr(.$cn, 2, nchar(.$cn)),
.$cn %in% ZM & .$cn %nin% filtro ~ str_wrap(paste(substr(estados[as.numeric(substr(.$cn, 1, 3))], 2, 3), "ZMVM"), 100),
.$cn %nin% ZM & substr(.$cn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]), 100),
.$cn %nin% ZM & substr(.$cn, 1, 3) %nin% filtro_est ~ "Otros estados")) %>%
filter(value > 0)
tabla1 <- tabla %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
# Grupo 1
grupo1 <- tabla %>%
filter(substr(.$rn, 1, 2) == "09") %>%
pull(rn) %>%
unique()
# Grupo 2
grupo2 <- tabla %>%
filter(substr(.$rn, 1, 2) == "15") %>%
pull(rn) %>%
unique()
# Grupo 3
grupo3 <- tabla %>%
filter(substr(.$rn, 1, 2) == "13") %>%
pull(rn) %>%
unique()
# Grupo 4
grupo4 <- tabla %>%
filter(substr(.$rn, 1, 2) %nin% c("09", "15", "13")) %>%
pull(rn) %>%
unique()
## Se guardan las matrices de movilidad laboral para analizarlos después.
tabla <- tabla1 %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(str_sort(unique(c(colnames(tabla1), rownames(tabla1))), numeric = TRUE)))
wb <- createWorkbook()
addWorksheet(wb, "ZMVM")
writeData(wb, 1, tabla, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a de ZMVM a nivel municipal_Reduccion.xlsx"),
overwrite = TRUE)# Paleta de colores
groupColors <- paste0(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1))))), "7F")
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a de ZMVM_grupos.pdf"),
width = 10, height = 10,
family = "Montserrat Medium",
fallback_resolution = 400,
onefile = TRUE)
circos.clear()
circos.par(start.degree = 90,
clock.wise = TRUE,
gap.degree = 3,
track.margin = c(-0.2, 0.2),
points.overflow.warning = FALSE)
par(mar = rep(0, 4))
# Paleta de colores
groupColors <- setNames(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1))))),
nm = unique(c(colnames(tabla1), rownames(tabla1))))
chordDiagram(x = tabla1 %>% as.matrix(),
grid.col = groupColors,
order = union(rownames(tabla1), colnames(tabla1)),
keep.diagonal = FALSE,
symmetric = FALSE,
scale = FALSE,
transparency = 0.25,
directional = 1,
direction.type = c("arrows", "diffHeight"),
diffHeight = -0.04,
annotationTrack = "grid",
annotationTrackHeight = mm_h(c(3)), # ancho borde
preAllocateTracks = 1,
big.gap = 40,
link.arr.type = "big.arrow",
link.lwd = 0.5,
link.visible = TRUE,
link.largest.ontop = FALSE)
# Add text and axis
circos.trackPlotRegion(track.index = 1,
track.height = 0.05,
bg.border = NA,
panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
# Add names to the sector.
circos.text(x = mean(xlim),
y = ylim[1] + 1, #Ajusta a las etiquetas del origen
labels = sector.name,
facing = "clockwise",
niceFacing = TRUE,
adj = c(-0.01, 0.5), #Ajuste de las etiquetas (x, y)
cex = fontsize(9),
col = "#000C7D",
font = 1)
# Add graduation on axis
circos.axis(h = "top",
labels = c(0, 10, 20, 50, 100, 200, 300, 400, 500, 1000, seq(1000, 20000, by = 100000)),
major.tick.length = 0.2,
minor.ticks = 2,
labels.cex = fontsize(7),
sector.index = sector.name,
track.index = 2,
labels.niceFacing = TRUE,
labels.pos.adjust = c(0, 0.8))
}
)
highlight.sector(grupo1,
track.index = 1,
col = groupColors[1],
text = "Ciudad de México",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0), niceFacing = TRUE)
highlight.sector(grupo2,
track.index = 1,
col = groupColors[15],
text = "México",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
highlight.sector(grupo3,
track.index = 1,
col = groupColors[20],
text = "Hidalgo",
cex = 1,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
highlight.sector(grupo4,
track.index = 1,
col = groupColors[30],
text = "Otro municipios",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
dev.off()Etiquetas
etiquetas <- lapply(1, function(x){
orden <- str_sort(unique(c(colnames(tabla1), rownames(tabla1))), numeric = TRUE)
p <- tabla1 %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
as.data.frame() %>%
mutate(rn = str_wrap(.$rn, 100),
cn = str_wrap(.$cn, 100)) %>%
ggplot() +
geom_bar(aes(x = value, fill = as.character(rn, orden))) +
geom_bar(aes(x = value, fill = as.character(cn, orden))) +
theme(plot.margin = margin(t = 1, r = 1.5, b = 1, l = 0, "cm"),
text = element_text(family = "Montserrat Medium"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 9, face = "bold", family = "Montserrat Medium"),
legend.key.size = unit(0.4, "cm"),
legend.spacing.y = unit(0.4, "cm"),
legend.text = element_text(size = 7, family = "Montserrat Medium"),
legend.title = element_text(size = 8 , family = "Montserrat Medium")) +
scale_fill_manual(values = colorRampPalette(paleta)(max(ncol(tabla1), nrow(tabla1)))) +
guides(fill = guide_legend(ncol = 1)) +
guides(fill = guide_legend(ncol = 1)) +
labs(fill = "ZM del Valle de México",
color = "ZM del Valle de México")
leg <- get_legend(p)
as_ggplot(leg)
})
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Etiquetas ZMVM a nivel municipal.pdf"),
width = 7, height = 9,
fallback_resolution = 400,
family = "montserrat", onefile = TRUE)
for(i in 1){
print(etiquetas[i])
}
dev.off()Gráfico Sankey
Zona Metropolitana del Valle de México
(ZMVM)
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 30)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 30)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 30)) %>%
pull(NOM_MUN)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = Inmigrantes + Emigrantes) %>%
filter(rn %in% ZM) %>%
filter(value < 50000) %>%
pull(rn)
### filtro de estados
filtro_rn <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & .$rn %in% ZM |.$cn %in% ZM, value, 0)) %>%
filter(value > 0) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(Inm = sum(value, na.rm = TRUE))
filtro_cn <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & .$rn %in% ZM |.$cn %in% ZM, value, 0)) %>%
filter(value > 0) %>%
mutate(cn = substr(.$cn, 1, 3)) %>%
group_by(cn) %>%
summarise(Emg = sum(value, na.rm = TRUE))
filtro_estados <- filtro_rn %>%
full_join(., filtro_cn, by = c("rn" = "cn")) %>%
mutate(value = sum_row(.$Inm, .$Emg, na.rm = TRUE)) %>%
filter(value > 50000) %>%
pull(rn)
################################################################################
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %nin% filtro ~ .$rn,
.$rn %in% ZM & .$rn %in% filtro ~ paste(substr(.$rn, 1, 3), "Municipios ZM"),
.$rn %nin% ZM & substr(.$rn, 1, 3) %in% filtro_estados ~ paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]),
.$rn %nin% ZM & substr(.$rn, 1, 3) %nin% filtro_estados ~ "Otros estados"),
cn = case_when(.$cn %in% ZM & .$cn %nin% filtro ~ .$cn,
.$cn %in% ZM & .$cn %in% filtro ~ paste(substr(.$cn, 1, 3), "Municipios ZM"),
.$cn %nin% ZM & substr(.$cn, 1, 3) %in% filtro_estados ~ paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]),
.$cn %nin% ZM & substr(.$cn, 1, 3) %nin% filtro_estados ~ "Otros estados")) %>%
filter(value > 0)
p <- tabla %>%
ggplot(aes(axis1 = rn,
axis2 = cn,
y = value), # c("value", "freq", "tasa")
reverse = FALSE,
na.rm = TRUE) +
geom_alluvium(aes(fill = rn),
curve_type = "quintic",
color = "transparent",
alpha = 0.85,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_stratum(aes(fill = cn),
color = "white",
alpha = 0.65,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 1, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = -.23,
min.segment.length = unit(1, "lines"),
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 2, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 2, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = .23,
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
theme_void() +
theme(plot.margin = margin(t = 1, r = 4, b = 1, l = 0, "cm"),
text = element_text(family = "montserrat"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 9, family = "montserrat"),
legend.position = c(0.999, .5)) +
scale_x_discrete(expand = c(-0.1, 0.5)) +
scale_fill_viridis_d(option = "A", end = 0.9, begin = 0.2) +
guides(fill = guide_legend(ncol = 1, na.translate = F)) +
labs(fill = "",
color = "")
path = paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/GSankey de MR5a de la ZMVM.pdf")
ggexport(p, width = 20, height = 12, dpi = 400, filename = path)Indicadores
Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta
Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora
################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
as.data.frame() %>%
group_by(CVE_MUN) %>%
summarise(Pob.Total = sum(FACTOR))
################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
as.data.frame() %>%
mutate(EDAD = as.numeric(.$EDAD)) %>%
subset(EDAD >= 5 & EDAD <=130) %>%
group_by(CVE_MUN) %>%
summarise(Pob.5ymas = sum(FACTOR))
################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
Residentes <- Migrantes %>%
rownames_to_column() %>%
gather(CVE_MUN, Value, -rowname)%>%
filter(rowname == CVE_MUN) %>%
select(-rowname) %>%
droplevels() %>%
rename("Residentes" = "Value")
################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
as_tibble() %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
group_by(CVE_MUN) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
################################################################################
############################### Emigrantes #####################################
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
as_tibble() %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
group_by(CVE_MUN_RES) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE)) %>%
rename("CVE_MUN" = "CVE_MUN_RES")
tabla <- Pob.Total %>%
left_join(., Pob.5ymas, by = c("CVE_MUN")) %>%
left_join(., Residentes, by = c("CVE_MUN")) %>%
left_join(., Inmigrantes, by = c("CVE_MUN")) %>%
left_join(., Emigrantes, by = c("CVE_MUN")) %>%
mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
Mig.Bruta = .$Inmigrantes + .$Emigrantes,
Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Mig = Tasa.Inmig - Tasa.Emig,
Eficacia = Mig.Neta - Mig.Bruta)
write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020.xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020.RData"))| Indicadores de migración reciente | |||||||||||
| Zonas Metropolitanas | |||||||||||
| Clave del municipio | Pob.Total | Pob.5ymas | Residentes | Inmigrantes | Emigrantes | Mig.Neta | Mig.Bruta | Tasa.Inmig | Tasa.Emig | Tasa.Mig | Eficacia |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||
Se genera la matriz cuadrada y se le asignan los nombres de los estados.
Migración intramunicipal
Matrices
Se utiliza la paquetería survey para poder trabajar con
la muestra del cuestionario ampliado, en la cual se selecciona a la
población de 5 años y más.
options(survey.lonely.psu = "adjust")
MC <- mydata %>%
select(CVE_ENT, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES, CVE_ZM, MC, CF, CVE_ZM_RES, M, EDAD, FACTOR, ESTRATO, UPM) %>%
# Se genera una indicadora de zm
mutate(I_ZM_2020 = ifelse(is.na(.$CVE_ZM), '0', '1'),
I_RES5A_ZM_2020 = ifelse(is.na(.$CVE_ZM_RES), '0', '1')) %>%
# Se clasifican a los migrantes internos
mutate(I_ZM = case_when(.$CVE_MUN == .$CVE_MUN_RES ~ 'Pertenecen a la Zona Metropolitana', #Residentes
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM == .$CVE_ZM_RES ~ "Pertenecen a la Zona Metropolitana", #Migrantes que residen en la misma zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM != .$CVE_ZM_RES ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residen en otra zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian fuera de la zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '1' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian dentro de una zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana' #Migrantes que no residen en la zona metropolitana
)) %>%
filter(EDAD >= 5 & EDAD <= 130) %>%
filter(CVE_MUN_RES %in% municipios & .$I_ZM %in% "Pertenecen a la Zona Metropolitana") %>%
svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)
saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_intramunicipal.RDS"))Se genera una matriz cruzada del lugar de residencia hace 5 años a
nivel municipal, utilizando la función svytable de la
paquetería survey.
MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_intramunicipal.RDS"))
Migrantes <- svytable(~CVE_MUN_RES + CVE_MUN, design = MC)Se genera la matriz cuadrada y se le asignan las etiquetas de municipios.
Migrantes <- Migrantes %>%
as.data.frame() %>%
expss::cross_cases(CVE_MUN, CVE_MUN_RES, weight = Freq) %>%
as.data.frame() %>%
rename("CVE_MUN" = "row_labels") %>%
arrange(CVE_MUN) %>%
slice(-1)
rownames <- Migrantes %>%
mutate(CVE_MUN = substr(.$CVE_MUN, 9, 16)) %>%
pull(CVE_MUN)
colnames <- names(Migrantes) %>%
as.data.frame() %>%
slice(-1) %>%
rename("CVE_MUN" = ".") %>%
mutate(`CVE_MUN` = substr(.$CVE_MUN, 13, 20)) %>%
pull(CVE_MUN)
# Se elimina la variable CVE_MUN
Migrantes <- Migrantes %>%
select(-CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
saveRDS(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.RDS"))
save(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))
require(openxlsx)
wb <- createWorkbook()
addWorksheet(wb, "M.Intramunicipal")
writeData(wb, 1, Migrantes %>% as.data.frame() %>% tibble::rownames_to_column(var = "CVE_MUN"), colNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.xlsx"), overwrite = TRUE)Matriz de migración reciente hace 5 años a nivel municipal, 2015 - 2020
| Matriz de migración reciente por zonas metropolitanas | |||||||||||||||||||||||||||||
| Nivel intramunicipal | |||||||||||||||||||||||||||||
| CVE_MUN | 001001 | 001002 | 001003 | 001004 | 001005 | 001006 | 001007 | 001008 | 001009 | 001010 | 001011 | 002001 | 002002 | 002003 | 002004 | 002005 | 002006 | 003001 | 003002 | 003003 | 003008 | 003009 | 004001 | 004002 | 004003 | 004004 | 004005 | 004006 | 004007 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||||||||||||||||||||
Matrices por zonas metropolitanas
MR <- NULL
for(i in 1:length(zm)){
tabla <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN) %>%
filter(CVE_ZM %in% zm[i]) %>%
pull(CVE_MUN)
MR[[paste0(zm[i])]] <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
mutate_if(is.numeric, as.numeric) %>%
select(CVE_MUN, all_of(tabla)) %>%
filter(CVE_MUN %in% tabla)
}
# Se guardan en un objeto de R
saveRDS(MR, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matrices de MR5a a nivel intramunicipal por ZM2020.RDS"))
# Se genera un Excel con todas las matrices por ZM
wb <- createWorkbook()
for(i in 1:length(zm)){
addWorksheet(wb, paste0(zm[i]))
writeData(wb, i, MR[[paste0(zm[i])]] %>% as.data.frame())
saveWorkbook(wb,
file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matrices de MR5a a nivel intramunicipal por ZM2020.xlsx"),
overwrite = TRUE)
}Matriz de migración reciente hace 5 años en la Zona Metropolitana de Cuernavaca, 2015 - 2020
| Matriz de migración reciente a nivel intramunicipal | ||||||||||
| Zona Metropolitana de Cuernavaca | ||||||||||
| CVE_MUN | 017007 | 017008 | 017009 | 017011 | 017018 | 017020 | 017028 | 017029 | 017034 | 017035 |
|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | ||||||||||
Gráficos
ChordDiagram
Gráficos por estados
Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
# Nombre de las Zonas Metropolitanas
NOM_ZM <- stringr::str_wrap(nom_zm, 100)
## Tomamos las Zonas Metropolitanas con más de 3 municipios que tienen flujos migratorios
#### Con filtro (CF)
ZM_CF <- ZM_2020 %>%
group_by(CVE_ZM) %>%
summarise(Count = n()) %>%
filter(Count >= 3) %>%
pull(CVE_ZM)
NOM_ZM_CF <- ZM_2020 %>%
filter(CVE_ZM %in% ZM_CF) %>%
distinct(CVE_ZM, NOM_ZM)
ZM <- lapply(1:length(ZM_CF), function(x){
ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% ZM_CF[x]) %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(NOM_MUN)
})
################################################################################
################################## Filtro ######################################
Inmigrantes <- Inmigrantes_function(ZM, Migrantes)
Emigrantes <- Emigrantes_function(ZM, Migrantes)
################################# Filtro #######################################
### Sacar el promedio de los flujos migratiorios para determinar como se van a grupar los estados
#### Es importante correr la tabla1[[x]] sin filtros para determinar el número promedio de flujos de migración
### Filtro <<<< filter(value > 0 & rn != estado[x])
#p <- data.frame(ZM = ZM_CF,
# filtro_municipio = tabla_municipios)
#write.xlsx(p, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel intramunicipal.xlsx"), overwrite = TRUE)
#### Filtro de municipios
filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel intramunicipal.xlsx"), colNames = TRUE) %>%
pull(filtro_municipio)
################################################################################
tabla1 <- lapply(1:length(ZM), function(x){
filtro <- Inmigrantes[[x]] %>%
full_join(., Emigrantes[[x]], by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value < filtro_mig[x]) %>%
pull(rn)
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]), value, 0)) %>%
mutate(rn = ifelse(.$rn %in% filtro,
stringr::str_wrap(paste0(substr(as.character(.$rn), 1, 3), " Otros municipios (", estados[as.numeric(substr(as.character(.$rn), 1, 3))], ")"), 100),
substr(.$rn, 2, nchar(.$rn))),
cn = ifelse(.$cn %in% filtro,
stringr::str_wrap(paste0(substr(as.character(.$cn), 1, 3), " Otros municipios (", estados[as.numeric(substr(as.character(.$cn), 1, 3))], ")"), 100),
substr(.$cn, 2, nchar(.$cn)))) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
})
## Se sacan los flujos migratorios que pertencen a otros municipios
#tabla_municipios <- sapply(1:length(ZM), function(i){
# tabla1[[i]] %>%
# as.data.frame() %>%
# adorn_totals(c("row", "col"),
# fill = "-",
# na.rm = TRUE,
# ,,,,contains(colnames(tabla1[[i]]))) %>%
# slice(nrow(.)) %>%
# mutate(Total = .$Total/50) %>%
# pull(Total)
#})
## Se guardan las matrices de Movilidad laboral para analizarlos después.
wb <- createWorkbook()
for(i in 1:length(ZM)){
tabla <- tabla1[[i]] %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(colnames(tabla1[[i]])))
addWorksheet(wb, paste(ZM_CF[i]))
writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel intramunicipal_Reduccion.xlsx"),
overwrite = TRUE)
}
saveRDS(tabla1, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel intramunicipal.RDS"))tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel intramunicipal.RDS"))
total_tablas <- totales(tabla1, ZM)
porcentajes_tablas <- porcentajes(tabla1, ZM)
# Se guardan los totales de las matrices reducidas
wb <- createWorkbook()
for(i in 1:length(ZM)){
addWorksheet(wb, paste(ZM_CF[i]))
writeData(wb, i, total_tablas[[i]], colNames = TRUE, startCol = 1)
writeData(wb, i, porcentajes_tablas[[i]], colNames = TRUE, startCol = 5)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel intramunicipal_Reduccion_Totales.xlsx"),
overwrite = TRUE)
}tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel intramunicipal.RDS"))
#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")
tabla2 <- color_chord_diagram(tabla1, ZM_CF = ZM_CF, paleta)file = "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a desagregado por ZM (Intramunicipal).pdf"
## Gráficos a nivel intramunicipal
chord_diagram_graph(file = file,
width = 15,
height = 10,
family <- "Montserrat Medium",
paleta = paleta,
ZM_CF = ZM_CF,
tabla1,
tabla2,
color_labels = "#000C7D",
margin = c(0, 0, 0, 0))Etiquetas
Gráfico Sankey
## Tomamos las Zonas Metropolitanas con más de 3 municipios con flujos migratorios
ZM_CF <- ZM_2020 %>%
group_by(CVE_ZM) %>%
summarise(Count = n()) %>%
filter(Count > 2) %>%
pull(CVE_ZM)
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn")
tabla1 <- lapply(1:length(ZM_CF), function(x){
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% ZM_CF[x]) %>%
mutate(NOM_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(NOM_MUN)
tabla %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0)
}
) p <- lapply(1:length(ZM_CF), function(x){
tabla1[[x]] %>%
ggplot(aes(axis1 = rn,
axis2 = cn,
y = value), # c("value", "freq", "tasa")
reverse = FALSE,
na.rm = TRUE) +
geom_alluvium(aes(fill = rn),
curve_type = "quintic",
color = "transparent",
alpha = 0.85,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_stratum(aes(fill = cn),
color = "white",
alpha = 0.65,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 1, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = -.2,
min.segment.length = unit(1, "lines"),
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 2, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 2, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = .2,
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
theme_void() +
theme(plot.margin = margin(t = 1, r = 1.5, b = 1, l = 0, "cm"),
text = element_text(family = "montserrat"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 9, family = "montserrat"),
legend.position = c(1, .5)) +
scale_x_discrete(expand = c(-0.1, 0.35)) +
scale_fill_viridis_d(option = "A", end = 1, begin = 0.2) +
guides(fill = guide_legend(ncol = 1, na.translate = F)) +
labs(fill = "",
color = "")
}
)
path = paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/GSankey de MR5a desagregado por ZM_Absolutos (Intramunicipal).pdf")
ggexport(list = p, width = 14, height = 10, dpi = 400, filename = path)ZMVM
ChordDiagram
ChorDiagram sin grupos
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(NOM_MUN)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value < 15000) %>%
pull(rn)
################################################################################
tabla1 <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM | .$cn %in% ZM) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
mutate(rn = ifelse(.$rn %in% filtro,
stringr::str_wrap(paste0(substr(as.character(.$rn), 1, 3), " Otros municipios (", estados[as.numeric(substr(as.character(.$rn), 1, 3))], ")"), 30),
substr(.$rn, 2, nchar(.$rn))),
cn = ifelse(.$cn %in% filtro,
stringr::str_wrap(paste0(substr(as.character(.$cn), 1, 3), " Otros municipios (", estados[as.numeric(substr(as.character(.$cn), 1, 3))], ")"), 30),
substr(.$cn, 2, nchar(.$cn)))) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")groupColors <- paste0(rev(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1)))))), "7F")
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a de ZMVM (Intramunicipal).pdf"),
width = 7, height = 7,
family = "Montserrat Medium",
fallback_resolution = 400,
onefile = TRUE)
circos.clear()
circos.par(start.degree = 90,
clock.wise = TRUE,
gap.degree = 3,
track.margin = c(-0.2, 0.2),
points.overflow.warning = FALSE)
par(mar = rep(1.5, 4))
# Paleta de colores
groupColors <- setNames(rev(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1)))))),
nm = unique(c(colnames(tabla1), rownames(tabla1))))
chordDiagram(x = tabla1 %>% as.matrix(),
grid.col = groupColors,
order = union(rownames(tabla1), colnames(tabla1)),
keep.diagonal = FALSE,
symmetric = FALSE,
scale = FALSE,
transparency = 0.25,
directional = 1,
direction.type = c("arrows", "diffHeight"),
diffHeight = -0.04,
annotationTrack = "grid",
annotationTrackHeight = mm_h(c(3)), # ancho borde
preAllocateTracks = 1,
big.gap = 40,
link.arr.type = "big.arrow",
link.lwd = 0.5,
link.visible = TRUE,
link.largest.ontop = FALSE)
# Add text and axis
circos.trackPlotRegion(track.index = 1,
track.height = 0.05,
bg.border = NA,
panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
# Add names to the sector.
circos.text(x = mean(xlim),
y = ylim[1] + 0.2, #Ajusta a las etiquetas del origen
labels = sector.name,
facing = "clockwise",
niceFacing = TRUE,
adj = c(-0.01, 0.5), #Ajuste de las etiquetas (x, y)
cex = fontsize(7),
col = "#000C7D",
font = 1)
# Add graduation on axis
circos.axis(h = "top",
labels = c(0, 10, 20, 50, 100, 200, 300, 400, 500, 1000, seq(1000, 20000, by = 100000)),
major.tick.length = 0.2,
minor.ticks = 2,
labels.cex = fontsize(5),
sector.index = sector.name,
track.index = 2,
labels.niceFacing = TRUE,
labels.pos.adjust = c(0, 0.8))
}
)
dev.off()ChordDiagram con grupos
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(NOM_MUN)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value < 15000) %>%
pull(rn)
################################################################################
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM | .$cn %in% ZM) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
mutate(rn = case_when(.$rn %in% filtro ~ stringr::str_wrap(paste(substr(as.character(.$rn), 2, 3), "Otros municipios (", estados[as.numeric(substr(as.character(.$rn), 1, 3))], ")"), 30),
.$rn %nin% filtro ~ substr(.$rn, 2, nchar(.$rn))),
cn = case_when(.$cn %in% filtro ~ stringr::str_wrap(paste(substr(as.character(.$cn), 2, 3), "Otros municipios (", estados[as.numeric(substr(as.character(.$cn), 1, 3))], ")"), 30),
.$cn %nin% filtro ~substr(.$cn, 2, nchar(.$cn)))) %>%
filter(value > 0)
tabla1 <- tabla %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
# Grupo 1
grupo1 <- tabla %>%
filter(substr(.$rn, 1, 2) == "09") %>%
pull(rn) %>%
unique()
# Grupo 2
grupo2 <- tabla %>%
filter(substr(.$rn, 1, 2) == "15") %>%
pull(rn) %>%
unique()
# Grupo 3
grupo3 <- tabla %>%
filter(substr(.$rn, 1, 2) == "13") %>%
pull(rn) %>%
unique()
## Se guardan las matrices de movilidad laboral para analizarlos después.
tabla <- tabla1 %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(colnames(tabla1)))
wb <- createWorkbook()
addWorksheet(wb, "ZMVM")
writeData(wb, 1, tabla, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a de ZMVM a nivel intramunicipal_Reduccion.xlsx"),
overwrite = TRUE)# Paleta de colores
groupColors <- paste0(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1))))), "7F")
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a de ZMVM_grupos (Intramunicipal).pdf"),
width = 10, height = 10,
family = "Montserrat Medium",
fallback_resolution = 400,
onefile = TRUE)
circos.clear()
circos.par(start.degree = 90,
clock.wise = TRUE,
gap.degree = 3,
track.margin = c(-0.2, 0.2),
points.overflow.warning = FALSE)
par(mar = rep(0, 4))
# Paleta de colores
groupColors <- setNames(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1))))),
nm = unique(c(colnames(tabla1), rownames(tabla1))))
chordDiagram(x = tabla1 %>% as.matrix(),
grid.col = groupColors,
order = union(rownames(tabla1), colnames(tabla1)),
keep.diagonal = FALSE,
symmetric = FALSE,
scale = FALSE,
transparency = 0.25,
directional = 1,
direction.type = c("arrows", "diffHeight"),
diffHeight = -0.04,
annotationTrack = "grid",
annotationTrackHeight = mm_h(c(3)), # ancho borde
preAllocateTracks = 1,
big.gap = 40,
link.arr.type = "big.arrow",
link.lwd = 0.5,
link.visible = TRUE,
link.largest.ontop = FALSE)
# Add text and axis
circos.trackPlotRegion(track.index = 1,
track.height = 0.05,
bg.border = NA,
panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
# Add names to the sector.
circos.text(x = mean(xlim),
y = ylim[1] + 1, #Ajusta a las etiquetas del origen
labels = sector.name,
facing = "clockwise",
niceFacing = TRUE,
adj = c(-0.01, 0.5), #Ajuste de las etiquetas (x, y)
cex = fontsize(9),
col = "#000C7D",
font = 1)
# Add graduation on axis
circos.axis(h = "top",
labels = c(0, 10, 20, 50, 100, 200, 300, 400, 500, 1000, seq(1000, 20000, by = 100000)),
major.tick.length = 0.2,
minor.ticks = 2,
labels.cex = fontsize(7),
sector.index = sector.name,
track.index = 2,
labels.niceFacing = TRUE,
labels.pos.adjust = c(0, 0.8))
}
)
highlight.sector(grupo1,
track.index = 1,
col = groupColors[1],
text = "Ciudad de México",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0), niceFacing = TRUE)
highlight.sector(grupo2,
track.index = 1,
col = groupColors[15],
text = "México",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
highlight.sector(grupo3,
track.index = 1,
col = groupColors[20],
text = "Hidalgo",
cex = 1,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
dev.off()Etiquetas
etiquetas <- lapply(1, function(x){
orden <- str_sort(unique(c(colnames(tabla1), rownames(tabla1))), numeric = TRUE)
p <- tabla1 %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
as.data.frame() %>%
mutate(rn = str_wrap(.$rn, 100),
cn = str_wrap(.$cn, 100)) %>%
ggplot() +
geom_bar(aes(x = value, fill = as.character(rn, orden))) +
geom_bar(aes(x = value, fill = as.character(cn, orden))) +
theme(plot.margin = margin(t = 1, r = 1.5, b = 1, l = 0, "cm"),
text = element_text(family = "Montserrat Medium"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 9, face = "bold", family = "Montserrat Medium"),
legend.key.size = unit(0.4, "cm"),
legend.spacing.y = unit(0.4, "cm"),
legend.text = element_text(size = 7, family = "Montserrat Medium"),
legend.title = element_text(size = 8 , family = "Montserrat Medium")) +
scale_fill_manual(values = colorRampPalette(paleta)(max(ncol(tabla1), nrow(tabla1)))) +
guides(fill = guide_legend(ncol = 1)) +
guides(fill = guide_legend(ncol = 1)) +
labs(fill = "ZM del Valle de México",
color = "ZM del Valle de México")
leg <- get_legend(p)
as_ggplot(leg)
})
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Etiquetas ZMVM a nivel intramunicipal.pdf"),
width = 7, height = 8,
fallback_resolution = 400,
family = "montserrat", onefile = TRUE)
for(i in 1){
print(etiquetas[i])
}
dev.off()Gráfico Sankey
Zona Metropolitana del Valle de México
(ZMVM)
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(NOM_MUN)
##########################################################################################
######################################## Filtro ##########################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
######################################## Filtro ##########################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = Inmigrantes + Emigrantes) %>%
filter(value < 30000) %>%
pull(rn)
#########################################################################################
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
mutate(rn = ifelse(.$rn %in% filtro, stringr::str_wrap(paste0(substr(as.character(.$rn), 1, 3), " Otros municipios(", estados[as.numeric(substr(as.character(.$rn), 1, 3))], ")"), 20), .$rn),
cn = ifelse(.$cn %in% filtro, stringr::str_wrap(paste0(substr(as.character(.$cn), 1, 3), " Otros municipios(", estados[as.numeric(substr(as.character(.$cn), 1, 3))], ")"), 20) , .$cn)) %>%
filter(value > 0)
p <- tabla %>%
ggplot(aes(axis1 = rn,
axis2 = cn,
y = value), # c("value", "freq", "tasa")
reverse = FALSE,
na.rm = TRUE) +
geom_alluvium(aes(fill = rn),
curve_type = "quintic",
color = "transparent",
alpha = 0.85,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_stratum(aes(fill = cn),
color = "white",
alpha = 0.65,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 1, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = -.23,
min.segment.length = unit(1, "lines"),
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 2, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 2, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = .23,
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
theme_void() +
theme(plot.margin = margin(t = 1, r = 4, b = 1, l = 0, "cm"),
text = element_text(family = "montserrat"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 9, family = "montserrat"),
legend.position = c(0.999, .5)) +
scale_x_discrete(expand = c(-0.1, 0.5)) +
scale_fill_viridis_d(option = "A", end = 0.9, begin = 0.2) +
guides(fill = guide_legend(ncol = 1, na.translate = F)) +
labs(fill = "",
color = "")
path = paste0(here::here(),"/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/GSankey de MR5a de la ZMVM (Intramunicipal).pdf")
ggexport(p, width = 20, height = 12, dpi = 400, filename = path)Indicadores
Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta
Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora
################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
as.data.frame() %>%
group_by(CVE_MUN) %>%
summarise(Pob.Total = sum(FACTOR))
################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
as.data.frame() %>%
mutate(EDAD = as.numeric(.$EDAD)) %>%
subset(EDAD >= 5 & EDAD <=130) %>%
group_by(CVE_MUN) %>%
summarise(Pob.5ymas = sum(FACTOR))
################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))
Residentes <- Migrantes %>%
rownames_to_column() %>%
gather(CVE_MUN, Value, -rowname)%>%
filter(rowname == CVE_MUN) %>%
select(-rowname) %>%
droplevels() %>%
rename("Residentes" = "Value")
################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
as_tibble() %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
group_by(CVE_MUN) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
################################################################################
############################### Emigrantes #####################################
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
as_tibble() %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
group_by(CVE_MUN_RES) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE)) %>%
rename("CVE_MUN" = "CVE_MUN_RES")
tabla <- Pob.Total %>%
left_join(., Pob.5ymas, by = c("CVE_MUN")) %>%
left_join(., Residentes, by = c("CVE_MUN")) %>%
left_join(., Inmigrantes, by = c("CVE_MUN")) %>%
left_join(., Emigrantes, by = c("CVE_MUN")) %>%
mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
Mig.Bruta = .$Inmigrantes + .$Emigrantes,
Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Mig = Tasa.Inmig - Tasa.Emig,
Eficacia = Mig.Neta - Mig.Bruta)
write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intramunicipal).xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intramunicipal).RData"))| Indicadores de migración reciente | |||||||||||
| Zonas Metropolitanas | |||||||||||
| Clave del municipio | Pob.Total | Pob.5ymas | Residentes | Inmigrantes | Emigrantes | Mig.Neta | Mig.Bruta | Tasa.Inmig | Tasa.Emig | Tasa.Mig | Eficacia |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||
Migración intermunicipal
Matrices
options(survey.lonely.psu = "adjust")
MC <- mydata %>%
select(CVE_ENT, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES, CVE_ZM, MC, CF, CVE_ZM_RES, M, EDAD, FACTOR, ESTRATO, UPM) %>%
# Se genera una indicadora de zm
mutate(I_ZM_2020 = ifelse(is.na(.$CVE_ZM), '0', '1'),
I_RES5A_ZM_2020 = ifelse(is.na(.$CVE_ZM_RES), '0', '1')) %>%
# Se clasifican a los migrantes internos
mutate(I_ZM = case_when(.$CVE_MUN == .$CVE_MUN_RES ~ 'Pertenecen a la Zona Metropolitana', #Residentes
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM == .$CVE_ZM_RES ~ "Pertenecen a la Zona Metropolitana", #Migrantes que residen en la misma zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM != .$CVE_ZM_RES ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residen en otra zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian fuera de la zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '1' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian dentro de una zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana' #Migrantes que no residen en la zona metropolitana
)) %>%
filter(EDAD >= 5 & EDAD <= 130) %>%
filter(CVE_MUN_RES %in% municipios & .$I_ZM %in% "No pertenecen a la Zona Metropolitana") %>%
svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)
saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_intermunicipal.RDS"))Se genera una matriz cruzada del lugar de residencia hace 5 años a
nivel municipal, utilizando la función svytable de la
paquetería survey.
MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_intermunicipal.RDS"))
Migrantes <- svytable(~CVE_MUN_RES + CVE_MUN, design = MC)Se genera la matriz cuadrada y se le asignan las etiquetas de municipios.
Migrantes <- Migrantes %>%
as.data.frame() %>%
expss::cross_cases(CVE_MUN, CVE_MUN_RES, weight = Freq) %>%
as.data.frame() %>%
rename("CVE_MUN" = "row_labels") %>%
arrange(CVE_MUN) %>%
slice(-1)
rownames <- Migrantes %>%
mutate(CVE_MUN = substr(.$CVE_MUN, 9, 16)) %>%
pull(CVE_MUN)
colnames <- names(Migrantes) %>%
as.data.frame() %>%
slice(-1) %>%
rename("CVE_MUN" = ".") %>%
mutate(`CVE_MUN` = substr(.$CVE_MUN, 13, 20)) %>%
pull(CVE_MUN)
# Se elimina la variable CVE_MUN
Migrantes <- Migrantes %>%
select(-CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
saveRDS(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RDS"))
save(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
require(openxlsx)
wb <- createWorkbook()
addWorksheet(wb, "M.Intermunicipal")
writeData(wb, 1, Migrantes %>% as.data.frame() %>% tibble::rownames_to_column(var = "CVE_MUN"), colNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.xlsx"), overwrite = TRUE)Matriz de migración reciente hace 5 años a nivel intermunicipal, 2015 - 2020
| Matriz de migración reciente por zonas metropolitanas | |||||||||||||||||||||||||||||
| Nivel intramunicipal | |||||||||||||||||||||||||||||
| CVE_MUN | 001001 | 001002 | 001003 | 001004 | 001005 | 001006 | 001007 | 001008 | 001009 | 001010 | 001011 | 002001 | 002002 | 002003 | 002004 | 002005 | 003001 | 003002 | 003003 | 003008 | 003009 | 004001 | 004002 | 004003 | 004004 | 004005 | 004006 | 004007 | 004008 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||||||||||||||||||||
Gráficos
ChordDiagram
Gráficos por estados
Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
# Nombre de las Zonas Metropolitanas
NOM_ZM <- stringr::str_wrap(nom_zm, 100)
## Tomamos las Zonas Metropolitanas con más de 3 municipios que tienen flujos migratorios
#### Con filtro (CF)
ZM_CF <- ZM_2020 %>%
group_by(CVE_ZM) %>%
summarise(Count = n()) %>%
filter(Count >= 0) %>%
pull(CVE_ZM)
NOM_ZM_CF <- ZM_2020 %>%
filter(CVE_ZM %in% ZM_CF) %>%
distinct(CVE_ZM, NOM_ZM)
ZM <- lapply(1:length(ZM_CF), function(x){
ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% ZM_CF[x]) %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 100)) %>%
pull(NOM_MUN)
})
################################################################################
################################## Filtro ######################################
Inmigrantes <- Inmigrantes_function(ZM, Migrantes)
Emigrantes <- Emigrantes_function(ZM, Migrantes)
################################## Filtro ######################################
#p <- data.frame(ZM = ZM_CF,
# filtro_municipio = filtro_mig,
# filtro_estado = filtro_out)
#write.xlsx(p, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel intermunicipal.xlsx"), overwrite = TRUE)
#### Filtro de municipios
filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel intermunicipal.xlsx"), colNames = TRUE) %>%
pull(filtro_municipio)
#### Filtro de estados
filtro_out <- read.xlsx(paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel intermunicipal.xlsx"), colNames = TRUE) %>%
pull(filtro_estado)
################################################################################
tabla1 <- lapply(1:length(ZM_CF), function(x){
# filtro de municipios de la ZM
filtro <- Inmigrantes[[x]] %>%
full_join(., Emigrantes[[x]], by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > filtro_mig[x]) %>% ## Cambia el filtro de los municipios de la Zonas Metropolitanas
pull(rn)
# filtro de estados
filtro_est <- Inmigrantes[[x]] %>%
full_join(., Emigrantes[[x]], by = c("rn" = "cn")) %>%
filter(rn %nin% ZM[[x]]) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(value = sum(value)) %>%
filter(value >= filtro_out[x]) %>%
pull(rn)
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM[[x]] & .$rn %in% filtro ~ substr(.$rn, 2, nchar(.$rn)),
.$rn %in% ZM[[x]] & .$rn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$rn, 1, 3))], "ZM"), 100),
.$rn %nin% ZM[[x]] & substr(.$rn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]), 100),
.$rn %nin% ZM[[x]] & substr(.$rn, 1, 3) %nin% filtro_est ~ "Otros estados"),
cn = case_when(.$cn %in% ZM[[x]] & .$cn %in% filtro ~ substr(.$cn, 2, nchar(.$cn)),
.$cn %in% ZM[[x]] & .$cn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$cn, 1, 3))], "ZM"), 100),
.$cn %nin% ZM[[x]] & substr(.$cn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]), 100),
.$cn %nin% ZM[[x]] & substr(.$cn, 1, 3) %nin% filtro_est ~ "Otros estados")) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
}
)
## Se sacan los flujos migratorios que pertencen a otros estados
#tabla_estados <- sapply(1:length(ZM_CF), function(i){
# tabla1[[i]] %>%
# as.data.frame() %>%
# adorn_totals(c("row", "col"),
# fill = "-",
# na.rm = TRUE,
# ,,,,contains(colnames(tabla1[[i]]))) %>%
# select(`Otros estados`) %>%
# slice(nrow(.)) %>%
# mutate(`Otros estados` = .$`Otros estados`/10) %>%
# pull(`Otros estados`)
#})
## Se sacan los flujos migratorios que pertencen a otros municipios
#tabla_municipios <- sapply(1:length(ZM_CF), function(i){
# p <- tabla1[[i]] %>%
# as.data.frame() %>%
# select(-c(`Otros estados`)) %>%
# slice(-nrow(.))
# if(sum(p) == 0) {
# return(0)
# } else {
# p %>%
# adorn_totals(c("row", "col"),
# fill = "-",
# na.rm = TRUE,
# ,,,,contains(colnames(tabla1[[i]]))) %>%
# slice(nrow(.)) %>%
# mutate(Total = .$Total/100) %>%
# pull(Total)
# }
#})
## Se guardan las matrices de migración reciente para analizarlos después.
wb <- createWorkbook()
for(i in 1:length(ZM)){
tabla <- tabla1[[i]] %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(colnames(tabla1[[i]])))
addWorksheet(wb, paste(ZM_CF[i]))
writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel intermunicipal_Reduccion.xlsx"),
overwrite = TRUE)
}
saveRDS(tabla1, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Tabla MR5a a nivel intermunicipal.RDS"))tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Tabla MR5a a nivel intermunicipal.RDS"))
total_tablas <- totales(tabla1, ZM)
porcentajes_tablas <- porcentajes(tabla1, ZM)
# Se guardan los totales de las matrices reducidas
wb <- createWorkbook()
for(i in 1:length(ZM)){
addWorksheet(wb, paste(ZM_CF[i]))
writeData(wb, i, total_tablas[[i]], colNames = TRUE, startCol = 1)
writeData(wb, i, porcentajes_tablas[[i]], colNames = TRUE, startCol = 5)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel intermunicipal_Reduccion_Totales.xlsx"),
overwrite = TRUE)
}tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Tabla MR5a a nivel intermunicipal.RDS"))
#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")
tabla2 <- color_chord_diagram(tabla1, ZM_CF = ZM_CF, paleta)file = "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a desagregado por ZM (Intermunicipal).pdf"
## Gráficos a nivel intermunicipal
chord_diagram_graph(file = file,
width = 15,
height = 10,
family <- "Montserrat Medium",
paleta = paleta,
ZM_CF = ZM_CF,
tabla1,
tabla2,
color_labels = "#000C7D",
margin = c(0, 0, 0, 0))Etiquetas
Gráfico Sankey
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN), by = c("CVE_MUN")) %>%
mutate(CVE_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
# Nombre de las Zonas Metropolitanas
NOM_ZM <- stringr::str_wrap(nom_zm, 25)
## Tomamos las Zonas Metropolitanas con más de 3 municipios que tienen flujos migratorios
#### Con filtro (CF)
ZM_CF <- ZM_2020 %>%
group_by(CVE_ZM) %>%
summarise(Count = n()) %>%
filter(Count > 3) %>%
pull(CVE_ZM)
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(value > 0)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > 0)
filtro_est <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(value > 0)
################################################################################
tabla1 <- lapply(1:length(ZM_CF), function(x){
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% ZM_CF[x]) %>%
mutate(NOM_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(NOM_MUN)
# filtro de municipios metropolitanos
filtro <- filtro %>%
filter(rn %in% ZM) %>%
filter(value < 1000) %>%
pull(rn)
# filtro de estados
filtro_rn <- filtro_est %>%
mutate(value = ifelse(.$rn %nin% filtro |.$cn %nin% filtro, value, 0)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(Inm = sum(value, na.rm = TRUE))
filtro_cn <- filtro_est %>%
mutate(value = ifelse(.$rn %nin% filtro |.$cn %nin% filtro, value, 0)) %>%
mutate(cn = substr(.$cn, 1, 3)) %>%
group_by(cn) %>%
summarise(Emg = sum(value, na.rm = TRUE))
filtro_estados <- filtro_rn %>%
full_join(., filtro_cn, by = c("rn" = "cn")) %>%
mutate(value = sum_row(.$Inm, .$Emg, na.rm = TRUE)) %>%
filter(value > 50000) %>%
pull(rn)
tabla %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), .$value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %nin% filtro ~ .$rn,
.$rn %in% ZM & .$rn %in% filtro ~ paste(substr(.$rn, 1, 3), "Municipios ZM"),
.$rn %nin% ZM & substr(.$rn, 1, 3) %in% filtro_estados ~ paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]),
.$rn %nin% ZM & substr(.$rn, 1, 3) %nin% filtro_estados ~ "Otros estados"),
cn = case_when(.$cn %in% ZM & .$cn %nin% filtro ~ .$cn,
.$cn %in% ZM & .$cn %in% filtro ~ paste(substr(.$cn, 1, 3), "Municipios ZM"),
.$cn %nin% ZM & substr(.$cn, 1, 3) %in% filtro_estados ~ paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]),
.$cn %nin% ZM & substr(.$cn, 1, 3) %nin% filtro_estados ~ "Otros estados")) %>%
filter(value > 0)
}
)p <- lapply(1:length(ZM_CF), function(x){
tabla1[[x]] %>%
ggplot(aes(axis1 = rn,
axis2 = cn,
y = value), # c("value", "freq", "tasa")
reverse = FALSE,
na.rm = TRUE) +
geom_alluvium(aes(fill = rn),
curve_type = "quintic",
color = "transparent",
alpha = 0.85,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_stratum(aes(fill = cn),
color = "white",
alpha = 0.65,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 1, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = -.2,
min.segment.length = unit(1, "lines"),
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 2, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 2, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = .2,
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
theme_void() +
theme(plot.margin = margin(t = 1, r = 5, b = 1, l = 0, "cm"),
text = element_text(family = "montserrat"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 7, family = "montserrat"),
legend.position = c(1.02, .5)) +
scale_x_discrete(expand = c(-0.1, 0.5)) +
scale_fill_viridis_d(option = "A", end = 1, begin = 0.2) +
guides(fill = guide_legend(ncol = 1, na.translate = F)) +
labs(fill = "",
color = "")
}
)
path = paste0(here::here(),"/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/GSankey de MR5a desagregado por ZM_Absolutos (Intermunicipal).pdf")
ggexport(list = p, width = 18, height = 10, dpi = 400, filename = path)ZMVM
ChordDiagram
ChorDiagram sin grupos
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(NOM_MUN)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
filter(rn %in% ZM) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > 11000) %>%
pull(rn)
filtro_est <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
filter(rn %nin% ZM) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(value = sum(value)) %>%
filter(value > 13000) %>%
pull(rn)
################################################################################
tabla1 <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM | .$cn %in% ZM) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM| .$cn %in% ZM), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %in% filtro ~ substr(.$rn, 2, nchar(.$rn)),
.$rn %in% ZM & .$rn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$rn, 1, 3))], "ZMVM"), 50),
.$rn %nin% ZM & substr(.$rn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]), 50),
.$rn %nin% ZM & substr(.$rn, 1, 3) %nin% filtro_est ~ "Otros estados"),
cn = case_when(.$cn %in% ZM & .$cn %in% filtro ~ substr(.$cn, 2, nchar(.$cn)),
.$cn %in% ZM & .$cn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$cn, 1, 3))], "ZMVM"), 50),
.$cn %nin% ZM & substr(.$cn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]), 50),
.$cn %nin% ZM & substr(.$cn, 1, 3) %nin% filtro_est ~ "Otros estados")) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn") # Grupo de colores
groupColors <- paste0(rev(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1)))))), "7F")
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a de ZMVM (Intermunicipal).pdf"),
width = 7, height = 7,
family = "Montserrat Medium",
fallback_resolution = 400,
onefile = TRUE)
circos.clear()
circos.par(start.degree = 90,
clock.wise = TRUE,
gap.degree = 3,
track.margin = c(-0.2, 0.2),
points.overflow.warning = FALSE)
par(mar = rep(1.5, 4))
# Paleta de colores
groupColors <- setNames(rev(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1)))))),
nm = unique(c(colnames(tabla1), rownames(tabla1))))
chordDiagram(x = tabla1 %>% as.matrix(),
grid.col = groupColors,
order = union(rownames(tabla1), colnames(tabla1)),
keep.diagonal = FALSE,
symmetric = FALSE,
scale = FALSE,
transparency = 0.25,
directional = 1,
direction.type = c("arrows", "diffHeight"),
diffHeight = -0.04,
annotationTrack = "grid",
annotationTrackHeight = mm_h(c(3)), # ancho borde
preAllocateTracks = 1,
big.gap = 40,
link.arr.type = "big.arrow",
link.lwd = 0.5,
link.visible = TRUE,
link.largest.ontop = FALSE)
# Add text and axis
circos.trackPlotRegion(track.index = 1,
track.height = 0.05,
bg.border = NA,
panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
# Add names to the sector.
circos.text(x = mean(xlim),
y = ylim[1] + 0.2, #Ajusta a las etiquetas del origen
labels = sector.name,
facing = "clockwise",
niceFacing = TRUE,
adj = c(-0.01, 0.5), #Ajuste de las etiquetas (x, y)
cex = fontsize(7),
col = "#000C7D",
font = 1)
# Add graduation on axis
circos.axis(h = "top",
labels = c(0, 10, 20, 50, 100, 200, 300, 400, 500, 1000, seq(1000, 20000, by = 100000)),
major.tick.length = 0.2,
minor.ticks = 2,
labels.cex = fontsize(5),
sector.index = sector.name,
track.index = 2,
labels.niceFacing = TRUE,
labels.pos.adjust = c(0, 0.8))
}
)
dev.off()ChordDiagram con grupos
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN %>% select(CVE_MUN, NOM_MUN)) %>%
mutate(CVE_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = stringr::str_wrap(paste(.$CVE_MUN, .$NOM_MUN), 50)) %>%
pull(NOM_MUN)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
################################## Filtro ######################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
filter(rn %in% ZM) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
filter(value > 11000) %>%
pull(rn)
filtro_est <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
filter(rn %nin% ZM) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
mutate(rn = substr(.$rn, 1, 3)) %>%
group_by(rn) %>%
summarise(value = sum(value)) %>%
filter(value > 15000) %>%
pull(rn)
################################################################################
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM | .$cn %in% ZM) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM| .$cn %in% ZM), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %in% filtro ~ substr(.$rn, 2, nchar(.$rn)),
.$rn %in% ZM & .$rn %nin% filtro ~ str_wrap(paste(substr(estados[as.numeric(substr(.$rn, 1, 3))], 2, 3), "ZMVM"), 100),
.$rn %nin% ZM & substr(.$rn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]), 100),
.$rn %nin% ZM & substr(.$rn, 1, 3) %nin% filtro_est ~ "Otros estados"),
cn = case_when(.$cn %in% ZM & .$cn %in% filtro ~ substr(.$cn, 2, nchar(.$cn)),
.$cn %in% ZM & .$cn %nin% filtro ~ str_wrap(paste(substr(estados[as.numeric(substr(.$cn, 1, 3))], 2, 3), "ZMVM"), 100),
.$cn %nin% ZM & substr(.$cn, 1, 3) %in% filtro_est ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]), 100),
.$cn %nin% ZM & substr(.$cn, 1, 3) %nin% filtro_est ~ "Otros estados")) %>%
filter(value > 0)
tabla1 <- tabla %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
# Grupo 1
grupo1 <- tabla %>%
filter(substr(.$rn, 1, 2) == "09") %>%
pull(rn) %>%
unique()
# Grupo 2
grupo2 <- tabla %>%
filter(substr(.$rn, 1, 2) == "15") %>%
pull(rn) %>%
unique()
# Grupo 3
grupo3 <- tabla %>%
filter(substr(.$rn, 1, 2) == "13") %>%
pull(rn) %>%
unique()
# Grupo 4
grupo4 <- tabla %>%
filter(substr(.$rn, 1, 2) %nin% c("09", "15", "13")) %>%
pull(rn) %>%
unique()
## Se guardan las matrices de movilidad laboral para analizarlos después.
tabla <- tabla1 %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(colnames(tabla1)))
wb <- createWorkbook()
addWorksheet(wb, "ZMVM")
writeData(wb, 1, tabla, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a de ZMVM a nivel intermunicipal_Reduccion.xlsx"),
overwrite = TRUE)# Paleta de colores
groupColors <- paste0(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1))))), "7F")
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a de ZMVM_grupos (Intermunicipal).pdf"),
width = 10, height = 10,
family = "Montserrat Medium",
fallback_resolution = 400,
onefile = TRUE)
circos.clear()
circos.par(start.degree = 90,
clock.wise = FALSE,
gap.degree = 3,
track.margin = c(-0.2, 0.2),
points.overflow.warning = FALSE)
par(mar = rep(0, 4))
# Paleta de colores
groupColors <- setNames(colorRampPalette(paleta)(length(unique(c(colnames(tabla1), rownames(tabla1))))),
nm = unique(c(colnames(tabla1), rownames(tabla1))))
chordDiagram(x = tabla1 %>% as.matrix(),
grid.col = groupColors,
order = union(rownames(tabla1), colnames(tabla1)),
keep.diagonal = FALSE,
symmetric = FALSE,
scale = FALSE,
transparency = 0.25,
directional = 1,
direction.type = c("arrows", "diffHeight"),
diffHeight = -0.04,
annotationTrack = "grid",
annotationTrackHeight = mm_h(c(3)), # ancho borde
preAllocateTracks = 1,
big.gap = 40,
link.arr.type = "big.arrow",
link.lwd = 0.5,
link.visible = TRUE,
link.largest.ontop = FALSE)
# Add text and axis
circos.trackPlotRegion(track.index = 1,
track.height = 0.05,
bg.border = NA,
panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
# Add names to the sector.
circos.text(x = mean(xlim),
y = ylim[1] + 1, #Ajusta a las etiquetas del origen
labels = sector.name,
facing = "clockwise",
niceFacing = TRUE,
adj = c(-0.01, 0.5), #Ajuste de las etiquetas (x, y)
cex = fontsize(9),
col = "#000C7D",
font = 1)
# Add graduation on axis
circos.axis(h = "top",
labels = c(0, 10, 20, 50, 100, 200, 300, 400, 500, 1000, seq(1000, 20000, by = 100000)),
major.tick.length = 0.2,
minor.ticks = 2,
labels.cex = fontsize(7),
sector.index = sector.name,
track.index = 2,
labels.niceFacing = TRUE,
labels.pos.adjust = c(0, 0.8))
}
)
highlight.sector(grupo1,
track.index = 1,
col = groupColors[1],
text = "Ciudad de México",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0), niceFacing = TRUE)
highlight.sector(grupo2,
track.index = 1,
col = groupColors[15],
text = "México",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
highlight.sector(grupo3,
track.index = 1,
col = groupColors[20],
text = "Hidalgo",
cex = 1,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
highlight.sector(grupo4,
track.index = 1,
col = groupColors[30],
text = "Otro municipios",
cex = 1.5,
text.col = "white",
padding = c(-0.5, 0, -0.2, 0),
niceFacing = TRUE)
dev.off()Etiquetas
etiquetas <- lapply(1, function(x){
orden <- str_sort(unique(c(colnames(tabla1), rownames(tabla1))), numeric = TRUE)
p <- tabla1 %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
as.data.frame() %>%
mutate(rn = str_wrap(.$rn, 100),
cn = str_wrap(.$cn, 100)) %>%
ggplot() +
geom_bar(aes(x = value, fill = as.character(rn, orden))) +
geom_bar(aes(x = value, fill = as.character(cn, orden))) +
theme(plot.margin = margin(t = 1, r = 1.5, b = 1, l = 0, "cm"),
text = element_text(family = "Montserrat Medium"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 9, face = "bold", family = "Montserrat Medium"),
legend.key.size = unit(0.4, "cm"),
legend.spacing.y = unit(0.4, "cm"),
legend.text = element_text(size = 7, family = "Montserrat Medium"),
legend.title = element_text(size = 8 , family = "Montserrat Medium")) +
scale_fill_manual(values = colorRampPalette(paleta)(max(ncol(tabla1), nrow(tabla1)))) +
guides(fill = guide_legend(ncol = 1)) +
guides(fill = guide_legend(ncol = 1)) +
labs(fill = "ZM del Valle de México",
color = "ZM del Valle de México")
leg <- get_legend(p)
as_ggplot(leg)
})
cairo_pdf(paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Etiquetas ZMVM a nivel intermunicipal.pdf"),
width = 7, height = 9,
fallback_resolution = 400,
family = "montserrat", onefile = TRUE)
for(i in 1){
print(etiquetas[i])
}
dev.off()Gráfico Sankey
Zona Metropolitana del Valle de México
(ZMVM)
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN, by = c("CVE_MUN")) %>%
mutate(CVE_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(CVE_MUN)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_MUN" = ".") %>%
left_join(., MUN, by = c("CVE_MUN")) %>%
mutate(CVE_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(CVE_MUN)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
## Se toma como referencia a la Zona Metropolitana del Valle de México
ZM <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% "09.01") %>%
mutate(NOM_MUN = paste(.$CVE_MUN, .$NOM_MUN)) %>%
pull(NOM_MUN)
##########################################################################################
######################################## Filtro ##########################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(rn) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
filter(value > 0) %>%
group_by(cn) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE))
######################################## Filtro ##########################################
filtro <- Inmigrantes %>%
full_join(., Emigrantes, by = c("rn" = "cn")) %>%
mutate(value = Inmigrantes + Emigrantes) %>%
filter(rn %in% ZM) %>%
filter(value > 30000) %>%
pull(rn)
#########################################################################################
tabla <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM | .$cn %in% ZM), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM & .$rn %in% filtro ~ .$rn,
.$rn %in% ZM & .$rn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$rn, 1, 3))], "ZMVM"), 20),
.$rn %nin% ZM & .$rn %nin% filtro ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$rn, 1, 3))]), 20)),
cn = case_when(.$cn %in% ZM & .$cn %in% filtro ~ .$cn,
.$cn %in% ZM & .$cn %nin% filtro ~ str_wrap(paste(estados[as.numeric(substr(.$cn, 1, 3))], "ZMVM"), 20),
.$cn %nin% ZM & .$cn %nin% filtro ~ str_wrap(paste0(nom_estados[as.numeric(substr(.$cn, 1, 3))]), 20))) %>%
filter(value > 0)
p <- tabla %>%
ggplot(aes(axis1 = rn,
axis2 = cn,
y = value), # c("value", "freq", "tasa")
reverse = FALSE,
na.rm = TRUE) +
geom_alluvium(aes(fill = rn),
curve_type = "quintic",
color = "transparent",
alpha = 0.85,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_stratum(aes(fill = cn),
color = "white",
alpha = 0.65,
lwd = 0.001,
width = 1/5,
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 1, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 1, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = -.23,
min.segment.length = unit(1, "lines"),
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
geom_text_repel(aes(label = ifelse(after_stat(x) == 2, paste0(as.character(after_stat(stratum)), ": ", prettyNum(count, big.mark = " ")), ""),
fontface = ifelse(after_stat(x) == 2, 'bold', 'plain')),
stat = "stratum",
size = 3,
direction = "y",
nudge_x = .23,
force = 1,
force_pull = 0,
family = "montserrat",
reverse = FALSE) +
theme_void() +
theme(plot.margin = margin(t = 1, r = 4, b = 1, l = 0, "cm"),
text = element_text(family = "montserrat"),
axis.text = element_blank(),
axis.title = element_blank(),
strip.text = element_text(size = 10, face = "bold", family = "montserrat"),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 9, family = "montserrat"),
legend.position = c(0.999, .5)) +
scale_x_discrete(expand = c(-0.1, 0.5)) +
scale_fill_viridis_d(option = "A", end = 0.9, begin = 0.2) +
guides(fill = guide_legend(ncol = 1, na.translate = F)) +
labs(fill = "",
color = "")
path = paste0(here::here(), "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/GSankey de MR5a de la ZMVM (Intermunicipal).pdf")
ggexport(p, width = 20, height = 12, dpi = 400, filename = path)Indicadores
Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta
Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora
################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
as.data.frame() %>%
group_by(CVE_MUN) %>%
summarise(Pob.Total = sum(FACTOR))
################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
as.data.frame() %>%
mutate(EDAD = as.numeric(.$EDAD)) %>%
subset(EDAD >= 5 & EDAD <= 130) %>%
group_by(CVE_MUN) %>%
summarise(Pob.5ymas = sum(FACTOR))
################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
Residentes <- Migrantes %>%
rownames_to_column() %>%
gather(CVE_MUN, Value, -rowname)%>%
filter(rowname == CVE_MUN) %>%
select(-rowname) %>%
droplevels() %>%
rename("Residentes" = "Value")
################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
as_tibble() %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
group_by(CVE_MUN) %>%
summarise(Inmigrantes = sum(value, na.rm = TRUE))
################################################################################
############################### Emigrantes #####################################
Emigrantes <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
as_tibble() %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
group_by(CVE_MUN_RES) %>%
summarise(Emigrantes = sum(value, na.rm = TRUE)) %>%
rename("CVE_MUN" = "CVE_MUN_RES")
tabla <- Pob.Total %>%
left_join(., Pob.5ymas, by = c("CVE_MUN")) %>%
left_join(., Residentes, by = c("CVE_MUN")) %>%
left_join(., Inmigrantes, by = c("CVE_MUN")) %>%
left_join(., Emigrantes, by = c("CVE_MUN")) %>%
mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
Mig.Bruta = .$Inmigrantes + .$Emigrantes,
Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Mig = Tasa.Inmig - Tasa.Emig,
Eficacia = Mig.Neta - Mig.Bruta)
write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intermunicipal).xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intermunicipal).RData"))| Indicadores de migración reciente | |||||||||||
| Zonas Metropolitanas | |||||||||||
| Clave del municipio | Pob.Total | Pob.5ymas | Residentes | Inmigrantes | Emigrantes | Mig.Neta | Mig.Bruta | Tasa.Inmig | Tasa.Emig | Tasa.Mig | Eficacia |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||
Migración metropolitana
Se utiliza la paquetería survey para poder trabajar con
la muestra del cuestionario ampliado, en la cual se selecciona a la
población de 5 años y más.
options(survey.lonely.psu = "adjust")
MC <- mydata %>%
select(CVE_ENT, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES, CVE_ZM, MC, CF, CVE_ZM_RES, M, EDAD, FACTOR, ESTRATO, UPM) %>%
# Se genera una indicadora de zm
mutate(I_ZM_2020 = ifelse(is.na(.$CVE_ZM), '0', '1'),
I_RES5A_ZM_2020 = ifelse(is.na(.$CVE_ZM_RES), '0', '1')) %>%
# Se clasifican a los migrantes internos
mutate(I_ZM = case_when(.$CVE_MUN == .$CVE_MUN_RES ~ 'Pertenecen a la Zona Metropolitana', #Residentes
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM == .$CVE_ZM_RES ~ "Pertenecen a la Zona Metropolitana", #Migrantes que residen en la misma zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM != .$CVE_ZM_RES ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residen en otra zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian fuera de la zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '1' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian dentro de una zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana' #Migrantes que no residen en la zona metropolitana
)) %>%
filter(EDAD >= 5 & EDAD <= 130) %>%
filter(CVE_MUN_RES %in% municipios) %>%
svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)
saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_metropolitana.RDS"))Matrices
Se genera una matriz cruzada del lugar de residencia hace 5 años a
nivel municipal, utilizando la función svytable de la
paquetería survey.
MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_metropolitana.RDS"))
Migrantes <- svytable(~CVE_ZM_RES + CVE_ZM, design = MC)Se genera la matriz cuadrada y se le asignan las etiquetas de municipios.
Migrantes <- Migrantes %>%
as.data.frame() %>%
expss::cross_cases(CVE_ZM, CVE_ZM_RES, weight = Freq) %>%
as.data.frame() %>%
rename("CVE_ZM" = "row_labels") %>%
arrange(CVE_ZM) %>%
slice(-1)
rownames <- Migrantes %>%
mutate(CVE_ZM = substr(.$CVE_ZM, 8, 12)) %>%
pull(CVE_ZM)
colnames <- names(Migrantes) %>%
as.data.frame() %>%
slice(-1) %>%
rename("CVE_ZM" = ".") %>%
mutate(`CVE_ZM` = substr(.$CVE_ZM, 12, 16)) %>%
pull(CVE_ZM)
# Se elimina la variable CVE_ZM
Migrantes <- Migrantes %>%
select(-CVE_ZM)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
saveRDS(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de MR5a a nivel metropolitano 2020.RDS"))
save(Migrantes, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de MR5a a nivel metropolitano 2020.RData"))
require(openxlsx)
wb <- createWorkbook()
addWorksheet(wb, "M.Metropolitano")
writeData(wb, 1, Migrantes %>% as.data.frame() %>% tibble::rownames_to_column(var = "CVE_ZM"), colNames = TRUE)
saveWorkbook(wb, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de MR5a a nivel metropolitano 2020.xlsx"), overwrite = TRUE)Matriz de migración reciente hace 5 años a nivel municipal, 2015 - 2020
| Matriz de migración reciente por zonas metropolitanas | |||||||||||||||||||||||||||||
| Nivel municipal | |||||||||||||||||||||||||||||
| CVE_ZM | 01.01 | 02.01 | 02.02 | 02.03 | 03.01 | 03.02 | 04.01 | 05.01 | 05.02 | 05.03 | 05.04 | 05.05 | 06.01 | 06.02 | 07.01 | 07.02 | 08.01 | 08.02 | 08.03 | 08.04 | 09.01 | 10.01 | 11.01 | 11.02 | 11.03 | 11.04 | 11.06 | 11.07 | 12.01 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||||||||||||||||||||
Gráficos
ChordDiagram
Gráficos por Zonas Metropolitanas
Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de MR5a a nivel metropolitano 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_ZM" = ".") %>%
left_join(., ZM_2020 %>% select(CVE_ZM, NOM_ZM) %>% distinct(CVE_ZM, NOM_ZM), by = c("CVE_ZM")) %>%
mutate(CVE_ZM = stringr::str_wrap(paste(.$CVE_ZM, .$NOM_ZM), 100)) %>%
pull(CVE_ZM)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_ZM" = ".") %>%
left_join(., ZM_2020 %>% select(CVE_ZM, NOM_ZM) %>% distinct(CVE_ZM, NOM_ZM), by = c("CVE_ZM")) %>%
mutate(CVE_ZM = stringr::str_wrap(paste(.$CVE_ZM, .$NOM_ZM), 100)) %>%
pull(CVE_ZM)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
# Nombre de las Zonas Metropolitanas
NOM_ZM <- stringr::str_wrap(nom_zm, 100)
################################################################################
tabla1 <- lapply(1, function(x){
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
mutate(value = ifelse((.$rn != .$cn), value, 0)) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
}
)#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")
tabla2 <- color_chord_diagram(tabla1, ZM_CF = 1, paleta)file = "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a desagregado por ZM (metropolitano).pdf"
## Gráficos a nivel metropolitano
chord_diagram_graph(file = file,
width = 10,
height = 10,
family = "Montserrat Medium",
paleta = paleta,
ZM_CF = 1, #Longuitud 1
tabla1,
tabla2,
color_labels = "#000C7D",
margin = c(0, 0, 0, 0))Gráficos por Zonas Metropolitanas
Se filtran los flujos migratorios que son exclusivos de los estados y que visualmente sean más interpretables.
load(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de MR5a a nivel metropolitano 2020.RData"))
rownames <- rownames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_ZM" = ".") %>%
left_join(., ZM_2020 %>% select(CVE_ZM, NOM_ZM) %>% distinct(CVE_ZM, NOM_ZM), by = c("CVE_ZM")) %>%
mutate(CVE_ZM = stringr::str_wrap(paste(.$CVE_ZM, .$NOM_ZM), 100)) %>%
pull(CVE_ZM)
colnames <- colnames(Migrantes) %>%
as.data.frame() %>%
rename("CVE_ZM" = ".") %>%
left_join(., ZM_2020 %>% select(CVE_ZM, NOM_ZM) %>% distinct(CVE_ZM, NOM_ZM), by = c("CVE_ZM")) %>%
mutate(CVE_ZM = stringr::str_wrap(paste(.$CVE_ZM, .$NOM_ZM), 100)) %>%
pull(CVE_ZM)
rownames(Migrantes) <- rownames
colnames(Migrantes) <- colnames
# Nombre de las Zonas Metropolitanas
ZM <- ZM_2020 %>%
select(CVE_ZM, NOM_ZM) %>%
distinct(CVE_ZM, NOM_ZM) %>%
mutate(CVE_ZM = stringr::str_wrap(paste(.$CVE_ZM, .$NOM_ZM), 100)) %>%
pull(CVE_ZM)
NOM_ZM_CF <- ZM_2020 %>%
filter(CVE_ZM %in% ZM_CF) %>%
distinct(CVE_ZM, NOM_ZM)
################################################################################
################################## Filtro ######################################
Inmigrantes <- Inmigrantes_function(ZM, Migrantes)
Emigrantes <- Emigrantes_function(ZM, Migrantes)
################################## Filtro ######################################
#p <- data.frame(ZM = ZM,
# filtro_zm = filtro_mig)
#write.xlsx(p, file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel metropolitano.xlsx"), overwrite = TRUE)
#### Filtro de municipios
filtro_mig <- read.xlsx(paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Filtro a nivel metropolitano.xlsx"), colNames = TRUE) %>%
pull(filtro_zm)
################################################################################
tabla1 <- lapply(1:length(ZM), function(x){
# filtro de estados
filtro_zm <- Inmigrantes[[x]] %>%
full_join(., Emigrantes[[x]], by = c("rn" = "cn")) %>%
filter(rn %nin% ZM[[x]]) %>%
mutate(value = sum_row(Inmigrantes, Emigrantes, na.rm = TRUE)) %>%
mutate(rn = substr(.$rn, 1, 5)) %>%
group_by(rn) %>%
summarise(value = sum(value)) %>%
filter(value >= filtro_mig[x]) %>%
pull(rn)
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "rn") %>%
melt(., id.vars = "rn", variable.name = "cn") %>%
mutate_if(is.factor, as.character) %>%
filter(.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]) %>%
mutate(value = ifelse((.$rn != .$cn) & (.$rn %in% ZM[[x]] | .$cn %in% ZM[[x]]), value, 0)) %>%
mutate(rn = case_when(.$rn %in% ZM[[x]] ~ .$rn,
.$rn %nin% ZM[[x]] & substr(.$rn, 1, 5) %in% filtro_zm ~ .$rn,
.$rn %nin% ZM[[x]] & substr(.$rn, 1, 5) %nin% filtro_zm ~ "Otras zonas metropolitanas"),
cn = case_when(.$cn %in% ZM[[x]] ~ .$cn,
.$cn %nin% ZM[[x]] & substr(.$cn, 1, 5) %in% filtro_zm ~ .$cn,
.$cn %nin% ZM[[x]] & substr(.$cn, 1, 5) %nin% filtro_zm ~ "Otras zonas metropolitanas")) %>%
filter(value > 0) %>%
dcast(., rn ~ cn, value.var = "value", sum, na.rm = TRUE) %>%
column_to_rownames(., var = "rn")
}
)
## Se guardan las matrices de movilidad laboral para analizarlos después.
wb <- createWorkbook()
for(i in 1:length(ZM)){
tabla <- tabla1[[i]] %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(colnames(tabla1[[i]])))
addWorksheet(wb, zm[i])
writeData(wb, i, tabla, colNames = TRUE, rowNames = TRUE)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel metropolitano_Reduccion.xlsx"),
overwrite = TRUE)
}
saveRDS(tabla1, paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel metropolitano.RDS"))tabla1 <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel metropolitano.RDS"))
totales <- lapply(1:length(ZM), function(x){
#Se agrupan los totales
full_join(
tabla1[[x]] %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(str_sort(unique(c(colnames(tabla1[[x]]), rownames(tabla1[[x]]))), numeric = TRUE))) %>%
slice(nrow(.)) %>%
t() %>%
as.data.frame()%>%
rownames_to_column(var = "name") %>%
rename("Total_Filas" = "1"),
tabla1[[x]] %>%
as.data.frame() %>%
adorn_totals(c("row", "col"),
fill = "-",
na.rm = TRUE,
,,,,contains(str_sort(unique(c(colnames(tabla1[[x]]), rownames(tabla1[[x]]))), numeric = TRUE))) %>%
rownames_to_column(var = "name") %>%
select(name, Total) %>%
mutate(name = str_replace(.$name, "1$", "Total")),
by = c("name")
) %>%
rename("CVE_MUN" = "name",
"Emigrantes" = "Total_Filas",
"Inmigrantes" = "Total") %>%
select(Inmigrantes, CVE_MUN, Emigrantes)
}
)
porcentajes <- lapply(1:length(ZM), function(x){
#Se agrupan los porcentajes
full_join(
tabla1[[x]] %>%
as.data.frame() %>%
adorn_totals(c("col", "row"),
fill = "0",
na.rm = TRUE,
name = "Total",
,,,,contains(str_sort(unique(c(colnames(tabla1[[x]]), rownames(tabla1[[x]]))), numeric = TRUE))) %>%
adorn_percentages("col",
na.rm = TRUE) %>%
rownames_to_column(var = "name") %>%
select(name, Total) %>%
mutate(name = str_replace(.$name, "1$", "Total")),
tabla1[[x]] %>%
as.data.frame() %>%
adorn_totals(c("row"),
fill = "0",
na.rm = TRUE,
name = "Total",
,,,,contains(str_sort(unique(c(colnames(tabla1[[x]]), rownames(tabla1[[x]]))), numeric = TRUE))) %>%
adorn_percentages("row",
na.rm = TRUE,
,,,,contains(str_sort(unique(c(colnames(tabla1[[x]]), rownames(tabla1[[x]]))), numeric = TRUE))) %>%
slice(nrow(.)) %>%
t() %>%
as.data.frame()%>%
rownames_to_column(var = "name") %>%
rename("Total_Filas" = "1"),
by = c("name")
) %>%
rename("CVE_MUN" = "name",
"Emigrantes%" = "Total_Filas",
"Inmigrantes%" = "Total") %>%
select(`Inmigrantes%`, CVE_MUN, `Emigrantes%`)
}
)
# Se guardan los totales de las matrices reducidas
wb <- createWorkbook()
for(i in 1:length(ZM)){
addWorksheet(wb, paste(ZM_CF[i]))
writeData(wb, i, totales[[i]], colNames = TRUE, startCol = 1)
writeData(wb, i, porcentajes[[i]], colNames = TRUE, startCol = 5)
saveWorkbook(wb,
file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel metropolitano_Reduccion_Totales.xlsx"),
overwrite = TRUE)
}tabla1 <- readRDS(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz MR5a a nivel metropolitano.RDS"))
#paleta <- colorRampPalette(pals::kovesi.linear_bmy_10_95_c78(100))(50)
paleta <- c("#000C7D", "#00108D", "#02149C", "#1614A4", "#3012A6", "#5A0D9D", "#7A0895", "#910390", "#A7008A", "#BB0085", "#CE0080", "#DF047A", "#EE1774", "#FA2C6C", "#FD4364", "#FE595B", "#FF6F51", "#FF8445","#FF9636", "#FFA72B", "#FFB622")
tabla2 <- color_chord_diagram(tabla1, ZM_CF = ZM_CF, paleta)file = "/Graficos/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/ChordDiagram de MR5a desagregado por ZM (metropolitano)_individual.pdf"
## Gráficos a nivel metropolitano
chord_diagram_graph(file = file,
width = 8,
height = 8,
family <- "Montserrat Medium",
paleta = paleta,
ZM_CF = ZM_CF,
tabla1,
tabla2,
color_labels = "#000C7D",
margin = c(0, 0, 0, 0))Etiquetas
Indicadores
Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta
Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora
################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
as.data.frame() %>%
group_by(CVE_ZM) %>%
summarise(Pob.Total = sum(FACTOR))
################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
as.data.frame() %>%
mutate(EDAD = as.numeric(.$EDAD)) %>%
subset(EDAD >= 5 & EDAD <= 130) %>%
group_by(CVE_ZM) %>%
summarise(Pob.5ymas = sum(FACTOR))
################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel municipal.RData"))
ZM <- lapply(1:length(zm), function(x){
ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% zm[x]) %>%
pull(CVE_MUN)
})
Residentes <- lapply(1:length(zm), function(x){
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN == CVE_MUN_RES) %>%
filter(CVE_MUN %in% ZM[[x]]) %>%
summarize(Residentes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
}
)
Residentes <- do.call(rbind.data.frame, Residentes)
################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- lapply(1:length(zm), function(x){
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
filter(CVE_MUN %in% ZM[[x]]) %>%
summarize(Inmigrantes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
})
Inmigrantes <- do.call(rbind.data.frame, Inmigrantes)
################################################################################
############################### Emigrantes #####################################
Emigrantes <- lapply(1:length(zm), function(x){
Migrantes %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
filter(CVE_MUN %in% ZM[[x]]) %>%
summarize(Emigrantes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
})
Emigrantes <- do.call(rbind.data.frame, Emigrantes)
tabla <- Pob.Total %>%
left_join(., Pob.5ymas, by = c("CVE_ZM")) %>%
left_join(., Residentes, by = c("CVE_ZM")) %>%
left_join(., Inmigrantes, by = c("CVE_ZM")) %>%
left_join(., Emigrantes, by = c("CVE_ZM")) %>%
mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
Mig.Bruta = .$Inmigrantes + .$Emigrantes,
Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Mig = Tasa.Inmig - Tasa.Emig,
Eficacia = Mig.Neta - Mig.Bruta)
write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Metropolitano) 2020.xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Metropolitano) 2020.RData"))| Indicadores de migración reciente | |||||||||||
| Zonas Metropolitanas | |||||||||||
| CVE_ZM | Pob.Total | Pob.5ymas | Residentes | Inmigrantes | Emigrantes | Mig.Neta | Mig.Bruta | Tasa.Inmig | Tasa.Emig | Tasa.Mig | Eficacia |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||
Migración intrametropolitana
Se utiliza la paquetería survey para poder trabajar con
la muestra del cuestionario ampliado, en la cual se selecciona a la
población de 5 años y más.
options(survey.lonely.psu = "adjust")
MC <- mydata %>%
select(CVE_ENT, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES, CVE_ZM, MC, CF, CVE_ZM_RES, M, EDAD, FACTOR, ESTRATO, UPM) %>%
# Se genera una indicadora de zm
mutate(I_ZM_2020 = ifelse(is.na(.$CVE_ZM), '0', '1'),
I_RES5A_ZM_2020 = ifelse(is.na(.$CVE_ZM_RES), '0', '1')) %>%
# Se clasifican a los migrantes internos
mutate(I_ZM = case_when(.$CVE_MUN == .$CVE_MUN_RES ~ 'Pertenecen a la Zona Metropolitana', #Residentes
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM == .$CVE_ZM_RES ~ "Pertenecen a la Zona Metropolitana", #Migrantes que residen en la misma zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM != .$CVE_ZM_RES ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residen en otra zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian fuera de la zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '1' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian dentro de una zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana' #Migrantes que no residen en la zona metropolitana
)) %>%
filter(EDAD >= 5 & EDAD <= 130) %>%
filter(CVE_MUN_RES %in% municipios & .$I_ZM %in% "Pertenecen a la Zona Metropolitana") %>%
svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)
saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_intrametropolitana.RDS"))Matrices
Se genera una matriz cruzada del lugar de residencia hace 5 años a
nivel municipal, utilizando la función svytable de la
paquetería survey.
MC <- readRDS(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_intrametropolitana.RDS"))
Migrantes <- svytable(~CVE_MUN_RES + CVE_MUN, design = MC)Matrices por zonas metropolitanas
load(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intramunicipal 2020.RData"))
MR <- NULL
for(i in 1:length(zm)){
tabla <- ZM_2020 %>%
select(CVE_ZM, CVE_MUN) %>%
filter(CVE_ZM %in% zm[i]) %>%
pull(CVE_MUN)
MR[[paste0(zm[i])]] <- Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
mutate_if(is.numeric, as.numeric) %>%
select(CVE_MUN, all_of(tabla)) %>%
filter(CVE_MUN %in% tabla)
}
# Se guardan en un objeto de R
saveRDS(MR, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matrices de MR5a a nivel intramunicipal por ZM2020.RDS"))
# Se genera un Excel con todas las matrices por ZM
wb <- createWorkbook()
for(i in 1:length(zm)){
addWorksheet(wb, paste0(zm[i]))
writeData(wb, i, MR[[paste0(zm[i])]] %>% as.data.frame())
saveWorkbook(wb,
file = paste0(here::here(), "/Bases/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matrices de MR5a a nivel intramunicipal por ZM2020.xlsx"),
overwrite = TRUE)
}Matriz de migración reciente hace 5 años en la Zona Metropolitana de Cuernavaca, 2015 - 2020
| Matriz de migración reciente a nivel intramunicipal | ||||||||||
| Zona Metropolitana de Cuernavaca | ||||||||||
| CVE_MUN | 017007 | 017008 | 017009 | 017011 | 017018 | 017020 | 017028 | 017029 | 017034 | 017035 |
|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | ||||||||||
Indicadores
Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta
Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora
################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
as.data.frame() %>%
group_by(CVE_ZM) %>%
summarise(Pob.Total = sum(FACTOR))
################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
as.data.frame() %>%
mutate(EDAD = as.numeric(.$EDAD)) %>%
subset(EDAD >= 5 & EDAD <=130) %>%
group_by(CVE_ZM) %>%
summarise(Pob.5ymas = sum(FACTOR))
################################################################################
########################### Residentes #########################################
MR <- readRDS(paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matrices de MR5a a nivel intramunicipal por ZM2020.RDS"))
Residentes <- lapply(1:length(zm), function(x){
MR[[x]] %>%
as.data.frame() %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN == CVE_MUN_RES) %>%
summarize(Residentes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
}
)
Residentes <- do.call(rbind.data.frame, Residentes)
################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- lapply(1:length(zm), function(x){
MR[[x]] %>%
as.data.frame() %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
summarize(Inmigrantes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
})
Inmigrantes <- do.call(rbind.data.frame, Inmigrantes)
################################################################################
############################### Emigrantes #####################################
Emigrantes <- lapply(1:length(zm), function(x){
MR[[x]] %>%
tibble::column_to_rownames(var = "CVE_MUN") %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
summarize(Emigrantes= sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
})
Emigrantes <- do.call(rbind.data.frame, Emigrantes)
tabla <- Pob.Total %>%
left_join(., Pob.5ymas, by = c("CVE_ZM")) %>%
left_join(., Residentes, by = c("CVE_ZM")) %>%
left_join(., Inmigrantes, by = c("CVE_ZM")) %>%
left_join(., Emigrantes, by = c("CVE_ZM")) %>%
mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
Mig.Bruta = .$Inmigrantes + .$Emigrantes,
Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Mig = Tasa.Inmig - Tasa.Emig,
Eficacia = Mig.Neta - Mig.Bruta)
write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intrametropolitano) 2020.xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intrametropolitano) 2020.RData"))| Indicadores de migración reciente | |||||||||||
| Zonas Intrametropolitano | |||||||||||
| CVE_ZM | Pob.Total | Pob.5ymas | Residentes | Inmigrantes | Emigrantes | Mig.Neta | Mig.Bruta | Tasa.Inmig | Tasa.Emig | Tasa.Mig | Eficacia |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||
Migración intermetropolitana
Se utiliza la paquetería survey para poder trabajar con
la muestra del cuestionario ampliado, en la cual se selecciona a la
población de 5 años y más.
options(survey.lonely.psu = "adjust")
MC <- mydata %>%
select(CVE_ENT, CVE_MUN, ENT_PAIS_RES_5A, MUN_RES_5A, CVE_MUN_RES, CVE_ZM, MC, CF, CVE_ZM_RES, M, EDAD, FACTOR, ESTRATO, UPM) %>%
# Se genera una indicadora de zm
mutate(I_ZM_2020 = ifelse(is.na(.$CVE_ZM), '0', '1'),
I_RES5A_ZM_2020 = ifelse(is.na(.$CVE_ZM_RES), '0', '1')) %>%
# Se clasifican a los migrantes internos
mutate(I_ZM = case_when(.$CVE_MUN == .$CVE_MUN_RES ~ 'Pertenecen a la Zona Metropolitana', #Residentes
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM == .$CVE_ZM_RES ~ "Pertenecen a la Zona Metropolitana", #Migrantes que residen en la misma zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '1' & .$CVE_ZM != .$CVE_ZM_RES ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residen en otra zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '1' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian fuera de la zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '1' ~ 'No pertenecen a la Zona Metropolitana', #Migrantes que residian dentro de una zona metropolitana
.$CVE_MUN != .$CVE_MUN_RES & .$I_ZM_2020 %in% '0' & .$I_RES5A_ZM_2020 %in% '0' ~ 'No pertenecen a la Zona Metropolitana' #Migrantes que no residen en la zona metropolitana
)) %>%
filter(EDAD >= 5 & EDAD <= 130) %>%
filter(CVE_MUN_RES %in% municipios & .$I_ZM %in% "No pertenecen a la Zona Metropolitana") %>%
svydesign(data = ., id = ~ UPM, strata = ~ESTRATO, weight = ~FACTOR, nest = T)
saveRDS(MC, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/MC_intermetropolitana.RDS"))Indicadores
Se realizan cálculos generales de migración
- Residentes
- Inmigrantes
- Emigrantes
- % Inmigrantes
- % Emigrante
- Migración bruta
- Migración Neta
- % Tasa de migración bruta
- % Tasa de migración neta
Se trabaja con la matriz cuadrada, la cual de esta manera no se satura la computadora
################################################################################
############################ Población total ###################################
Pob.Total <- mydata %>%
as.data.frame() %>%
group_by(CVE_ZM) %>%
summarise(Pob.Total = sum(FACTOR))
################################################################################
###################### Población de 5 años y más ###############################
Pob.5ymas <- mydata %>%
as.data.frame() %>%
mutate(EDAD = as.numeric(.$EDAD)) %>%
subset(EDAD >= 5 & EDAD <=130) %>%
group_by(CVE_ZM) %>%
summarise(Pob.5ymas = sum(FACTOR))
################################################################################
########################### Residentes #########################################
load(file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Matriz de migracion reciente a nivel intermunicipal 2020.RData"))
ZM <- lapply(1:length(zm), function(x){
ZM_2020 %>%
select(CVE_ZM, CVE_MUN, NOM_MUN) %>%
filter(CVE_ZM %in% zm[x]) %>%
pull(CVE_MUN)
})
Residentes <- lapply(1:length(zm), function(x){
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN == CVE_MUN_RES) %>%
filter(CVE_MUN %in% ZM[[x]]) %>%
summarize(Residentes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
}
)
Residentes <- do.call(rbind.data.frame, Residentes)
################################################################################
############################### Inmigrantes ####################################
Inmigrantes <- lapply(1:length(zm), function(x){
Migrantes %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
filter(CVE_MUN %in% ZM[[x]]) %>%
summarize(Inmigrantes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
})
Inmigrantes <- do.call(rbind.data.frame, Inmigrantes)
################################################################################
############################### Emigrantes #####################################
Emigrantes <- lapply(1:length(zm), function(x){
Migrantes %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "CVE_MUN") %>%
melt(., id.vars = "CVE_MUN", variable.name = "CVE_MUN_RES") %>%
mutate_at(vars(3), as.numeric) %>%
filter(CVE_MUN != CVE_MUN_RES) %>%
filter(CVE_MUN %in% ZM[[x]]) %>%
summarize(Emigrantes = sum(value, na.rm = TRUE)) %>%
mutate(CVE_ZM = !!paste0(zm[x]))
})
Emigrantes <- do.call(rbind.data.frame, Emigrantes)
tabla <- Pob.Total %>%
left_join(., Pob.5ymas, by = c("CVE_ZM")) %>%
left_join(., Residentes, by = c("CVE_ZM")) %>%
left_join(., Inmigrantes, by = c("CVE_ZM")) %>%
left_join(., Emigrantes, by = c("CVE_ZM")) %>%
mutate(Mig.Neta = .$Inmigrantes - .$Emigrantes,
Mig.Bruta = .$Inmigrantes + .$Emigrantes,
Tasa.Inmig = ((.$Inmigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Emig = ((.$Emigrantes/ 5) /((.$Pob.Total + .$Pob.5ymas) / 2)) * 1000,
Tasa.Mig = Tasa.Inmig - Tasa.Emig,
Eficacia = Mig.Neta - Mig.Bruta)
write.xlsx(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intermetropolitano) 2020.xlsx"), overwrite = TRUE)
save(tabla, file = paste0(here::here(), "/Output/Municipio/06_Zonas Metropolitanas/2020/01_Migracion reciente/Indicadores de MR5a por ZM 2020 (Intermetropolitano) 2020.RData"))| Indicadores de migración reciente | |||||||||||
| Zonas Intermetropolitano | |||||||||||
| CVE_ZM | Pob.Total | Pob.5ymas | Residentes | Inmigrantes | Emigrantes | Mig.Neta | Mig.Bruta | Tasa.Inmig | Tasa.Emig | Tasa.Mig | Eficacia |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fuente: Estimaciones del CONAPO. | |||||||||||
Referencias
Librerias que se usaron en el documento
| package | loadedversion | source |
|---|---|---|
| Cairo | 1.6-1 | CRAN (R 4.3.1) |
| chorddiag | 0.1.3 | Github (mattflor/chorddiag@1688d72cd93071abb373e054190363bdfb3af2af) |
| circlize | 0.4.15 | CRAN (R 4.3.1) |
| doMC | 1.3.5 | R-Forge (R 4.3.1) |
| dplyr | 1.1.3 | CRAN (R 4.3.2) |
| expss | 0.11.6 | CRAN (R 4.3.1) |
| extrafont | 0.19 | CRAN (R 4.3.0) |
| foreach | 1.5.2 | CRAN (R 4.3.1) |
| ggalluvial | 0.12.5 | CRAN (R 4.3.1) |
| ggplot2 | 3.4.3 | CRAN (R 4.3.1) |
| ggpubr | 0.6.0 | CRAN (R 4.3.1) |
| ggrepel | 0.9.3 | CRAN (R 4.3.1) |
| ggsankey | 0.0.99999 | Github (davidsjoberg/ggsankey@3e171a83a5364bb24df7cb2cd9203dd79b1dae29) |
| gt | 0.10.0 | CRAN (R 4.3.1) |
| haven | 2.5.3 | CRAN (R 4.3.1) |
| Hmisc | 5.1-0 | CRAN (R 4.3.1) |
| iterators | 1.0.14 | CRAN (R 4.3.1) |
| janitor | 2.2.0 | CRAN (R 4.3.1) |
| kableExtra | 1.3.4 | CRAN (R 4.3.1) |
| knitr | 1.45 | CRAN (R 4.3.2) |
| maditr | 0.8.3 | CRAN (R 4.3.1) |
| Matrix | 1.6-1.1 | CRAN (R 4.3.1) |
| network | 1.18.1 | CRAN (R 4.3.1) |
| openxlsx | 4.2.5.2 | CRAN (R 4.3.1) |
| reshape2 | 1.4.4 | CRAN (R 4.3.1) |
| showtext | 0.9-6 | CRAN (R 4.3.1) |
| showtextdb | 3.0 | CRAN (R 4.3.1) |
| sjlabelled | 1.2.0 | CRAN (R 4.3.1) |
| sna | 2.7-1 | CRAN (R 4.3.1) |
| srvyr | 1.2.0 | CRAN (R 4.3.1) |
| statnet.common | 4.9.0 | CRAN (R 4.3.1) |
| stringr | 1.5.0 | CRAN (R 4.3.1) |
| survey | 4.2 | Github (bschneidr/fastsurvey@5e4df7bd6c4bac44fa9c6681db40c496dd701f45) |
| survival | 3.5-5 | CRAN (R 4.3.1) |
| sysfonts | 0.8.8 | CRAN (R 4.3.1) |
| tibble | 3.2.1 | CRAN (R 4.3.1) |
| tidyr | 1.3.0 | CRAN (R 4.3.1) |
This
work by Diana Villasana
Ocampo is licensed under a
Creative
Commons Attribution 4.0 International License.